# Variational PDE Models in Image Processing

```Variational PDE Models
in Image Processing
Tony F. Chan, Jianhong (Jackie) Shen, and Luminita Vese

I
mage processing, traditionally an engineering           as fingerprints and face identification). All these
field, has attracted the attention of many math-        highly diversified disciplines have made it neces-
ematicians during the past two decades. From            sary to develop common mathematical founda-
the point of view of vision and cognitive sci-          tions and frameworks for image analysis and
ence, image processing is a basic tool used to          processing. Mathematics at all levels must be in-
reconstruct the relative order, geometry, topology,           troduced to address the crucial criteria demanded
patterns, and dynamics of the three-dimensional               by this new era—genericity, well-posedness, accu-
(3-D) world from two-dimensional (2-D) images.                racy, and computational efficiency, just to name a
Therefore, it cannot be merely a historical coinci-           few. In return, image processing has created
dence that mathematics must meet image process-               tremendous opportunities for mathematical mod-
ing in this era of digital technology.                        eling, analysis, and computation.
the broad range of applications of image process-             ical image processing through one of the most re-
ing in contemporary science and technology. These             cent and very successful approaches—the varia-
applications include astronomy and aerospace ex-              tional PDE (partial differential equation) method.
ploration, medical imaging, molecular imaging,                We first discuss two crucial ingredients for image
computer graphics, human and machine vision,                  processing: image modeling or representation, and
telecommunication, autopiloting, surveillance                 processor modeling. We then focus on the varia-
video, and biometric security identification (such            tional PDE method. The backbone of the article
consists of two major problems in image process-
This paper is based on a plenary presentation given by        ing that we personally have worked on: inpainting
Tony F. Chan at the 2002 Joint Mathematics Meetings in        and segmentation. By no means, however, do we
San Diego. The work was supported in part by NSF grants       intend to give a comprehensive review of the en-
DMS-9973341 (Chan), DMS-0202565 (Shen), and ITR-
tire field of image processing. Many of the authors’
0113439 (Vese); by ONR grant N00014-02-1-0015 (Chan);
and by NIH grant NIH-P20MH65166 (Chan and Vese).
articles and preprints related to the subject of this
paper can be found online at our group home-
Tony F. Chan is professor of mathematics at the Univer-
page , where an extended bibliography is also
sity of California, Los Angeles. His email address is
chan@math.ucla.edu.                                           available.
Jianhong (Jackie) Shen is assistant professor at the School   Image Processing as an Input-Output System
of Mathematics, University of Minnesota. His email address    Directly connected to image processing are two
is jhshen@math.umn.edu.                                       dual fields in contemporary computer science:
Luminita Vese is assistant professor of mathematics at the    computer vision and computer graphics. Vision
University of California, Los Angeles. Her email address      (whether machine or human) tries to reconstruct
is lvese@math.ucla.edu.                                       the 3-D world from observed 2-D images, while

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```T                                      Q0                   Q
graphics pursues the opposite direction by de-
signing suitable 2-D scene images to simulate our        denoising+ deblurring         u0 = Ku + n         clean & sharp u
3-D world. Image processing is the crucial middle
inpainting                        u0 |Ω\D        entire image u|Ω
way connecting the two.
Abstractly, image processing can be considered        segmentation                           u0              “objects”
as an input-output system                                                                                [uk , Ωk ], k = 1, 2, . . .
scale-space                            u0       multiscale images
                
Q0 → Image Processor T        → Q                                                                 uλ1 , uλ2 , ...
(1)    (2)
Here T denotes a typical image processor: for ex-        motion estimation             (u0 , u0 , ...)       optical flows
ample, denoising, deblurring, segmentation, com-                                                               v (1) , v
(       (2) , ...)
pression, or inpainting. The input data Q0 can rep-
resent an observed or measured single image or
image sequence, and the output Q = (q1 , q2 , · · · )   Table 1. Typical image processors and their inputs and
contains all the targeted image features.               outputs. The symbols represent (1) K: a blurring kernel, and n:
For example, the human visual system can be         an additive noise, both assumed in this paper to be linear for
considered as a highly involved multilevel image        simplicity; (2) u0 : the given noisy or blurred image; (3) Ω : the
processor T . The input Q0 represents the image se-     entire image domain, and D : a subset where image
quence that is constantly projected onto the retina.    information is missing or inaccessible; (4) [uk , Ωk ] : Ωk ’s are the
The output vector Q contains all the major features     segmented individual “objects”, while uk ’s are their intensity
that are important to our daily life, from the low-     values; (5) λk ’s are different scales, and uλ can be roughly
level ones such as relative orders, shapes, and         understood as the projection of the input image at scale λ; (6)
(n)
grouping rules to high-level feature parameters         u0 ’s denote the discrete sampling of a continuous “movie”
that help classify or identify various patterns and     u0 (x, t) (with some small time step h) and v (n)’s are the
objects.                                                estimated optical flows (i.e., velocity fields) at each moment.
Table 1 lists some typical image processing
problems.                                                   Wavelet Representation. An image is often ac-
The two main ingredients of image processing        quired from the responses of a collection of
are the input Q0 and the processor T . As a result,     microsensors (or photo receptors), either digital or
the two key issues that have been driving main-         biological. During the past two decades, it has been
stream mathematical research on image process-          gradually realized (and experimentally supported)
ing are (a) the modeling and representation of the      that such local responses can be well approximated
input visual data Q0 , and (b) the modeling of the      by wavelets. This new representation tool has rev-
processing operators T . Although the two are in-       olutionized our notion of images and their multi-
dependent, they are closely connected to each other     scale structures . The new JPEG2000 protocol
by the universal rule in mathematics: the structure     for image coding and the successful compression
and performance of an operator T is greatly in-         of the FBI database of fingerprints are its two most
fluenced by how the input functions are modeled         influential applications. The theory is still being ac-
or represented.                                         tively pushed forward by a new generation of geo-
Image Modeling and Representation                       metric wavelets such as curvelets (Candés and
To efficiently handle and process images, we need       Donoho) and beamlets (Pennec and Mallat).
first to understand what images really are mathe-           Regularity Spaces. In the linear filtering theory of
matically and how to represent them. For example,       conventional digital image processing, an image u
is it adequate to treat them as general L2 functions    is considered to be in the Sobolev space H 1 (Ω) . The
or as a subset of L2 with suitable regularity con-      Sobolev model works well for homogeneous regions,
straints? Here we briefly outline three major classes   but it is insufficient as a global image model, since
of image modeling and representation.                   it “smears” the most important visual cue, namely,
Random fields modeling. An observed image u0        edges. Two well-known models have been intro-
is modeled as the sampling of a random field. For       duced to recognize the existence of edges. One is
example, the Ising spin model in statistical me-        the “object-edge” model of Mumford and Shah ,
chanics can be used to model binary images. More        and the other is the BV image model of Rudin, Osher,
generally, images are modeled by some Gibbs/Mar-        and Fatemi . The object-edge model assumes that
kovian random fields . The statistical proper-      an ideal image u consists of disjoint homogeneous
ties of fields are often established through a fil-     object patches [uk , Ωk ] with uk ∈ H 1 (Ωk ) and regu-
tering technique and learning theory. Random field      lar boundaries ∂Ωk (characterized by one-dimen-
modeling is the ideal approach for describing nat-      sional Hausdorff measure). The BV image model as-
ural images with rich texture patterns such as trees    sumes that   an ideal image has bounded total
and mountains.                                          variation Ω |Du|. Regularity-based image models

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```are generally applicable to images with low texture              Variational PDE Method
patterns and without rapidly oscillatory compo-                  Having briefly introduced the general picture of
nents.                                                           mathematical image processing, we now focus on
Modeling of Image Processors                                     the variational PDE method through two processors:
How images are modeled and represented very                      inpainting and segmentation.
much determines the way we model image proces-                      For the history and a detailed description of
current developments of the variational and PDE
sors. We shall illustrate this viewpoint through the
method in image and vision analysis, see two spe-
example of denoising u = T u0 : u0 = u + n , as-
cial issues in IEEE Trans. Image Processing [7 (3),
suming for simplicity that the white noise n is ad-
1998] and J. Visual Comm. Image Rep. [13 (1/2),
ditive and homogeneous, and there is no blurring
2002] and also two recent monographs , .
involved.
In the variational or “energy”-based models,
When images are represented by wavelets, the                  nonlinear PDEs emerge as one derives formal Euler-
denoising processor T is in some sense “diago-                   Lagrange equations or tries to locate local or global
nalized” and is equivalent to a simple engineering               minima by the gradient descent method. Some
on the individual wavelet components. This is a cel-             PDEs can be studied by the viscosity solution
ebrated result of Donoho and Johnstone on                        approach , while many others still remain open
threshold-based denoising schemes.                               to further theoretical investigation.
Under the statistical/random field modeling of                   Compared with other approaches, the varia-
images, the denoising processor T becomes MAP                    tional PDE method has remarkable advantages in
(Maximum A Posteriori) estimation. By Bayes’s for-               both theory and computation. First, it allows one
mula, the posterior probability given an observa-                to directly handle and process visually important
tion u0 is                                                       geometric features such as gradients, tangents,
p(u|u0 ) = p(u0 |u)p(u)/p(u0 ).                     curvatures, and level sets. It can also effectively sim-
ulate several visually meaningful dynamic
The denoising processor T is achieved by solving                 processes, such as linear and nonlinear diffusions
the MAP problem max p(u|u0 ) . Therefore, it is im-              and the information transport mechanism. Sec-
u
portant to know not only the random field image                  ond, in terms of computation, it can profoundly
model p(u) but also the mechanism by which u0 is                 benefit from the existing wealth of literature on
generated from the ideal image u (the so-called                  numerical analysis and computational PDEs. For
generative data model). The two are crucial for                  example, various well-designed shock-capturing
successfully carrying out Bayesian denoising.                    schemes in Computational Fluid Dynamics (CFD)
can be conveniently adapted to edge computation
Finally, if the ideal image u is modeled as an el-
in images.
ement in a regular function space such as H 1 (Ω)
or BV(Ω) , then the denoising processor T can be                 Variational Image Inpainting and
realized by a variational optimization. For instance,            Interpolation
in the BV image model, T is achieved by
The word inpainting is an artistic synonym for
                                                        image interpolation; initially it circulated among
1
min       |Du| subject to             (u − u0 )2 dx ≤ σ 2 ,     museum restoration artists who manually restore
u    Ω                     |Ω|   Ω                             cracked ancient paintings. The concept of digital
where the white noise is assumed to be well ap-                  inpainting was recently introduced into digital
image processing in a paper by Bertalmio, Sapiro,
proximated by the standard Gaussian N(0, σ 2 ) .
Caselles, and Ballester. Currently, digital inpaint-
This well-known denoising model, first proposed
ing techniques are finding broad applications in
by Rudin, Osher, and Fatemi, belongs to the more
image processing, vision analysis, and digital tech-
general class of regularized data-fitting models.
nologies such as image restoration, disocclusion,
Just as different coordinate systems that de-
perceptual image coding, zooming and image super-
scribe a single physical object are related, different           resolution, error concealment in wireless image
formulations of the same image processor are                     transmission, and so on , , . Figure 1 shows
closely interconnected. Again, take denoising for                an example of error concealment.
example. It has been shown that the wavelet tech-                   We now discuss the mathematical ideas and
nique is equivalent to an approximate optimal reg-               methodologies behind variational inpainting tech-
ularization in certain Besov spaces (Cohen, Dahmen,              niques. Throughout this section, u denotes the
Daubechies, and DeVore). On the other hand,                      original complete image on a 2-D domain Ω , and
Bayesian processing and the regularity-based                     u0 denotes the observed or measured portion of
variational approach can also be connected (at                   u, which can be either noisy or blurry, on a sub-
least formally) by Gibbs’s formula in statistical                domain or general subset D . The goal of inpaint-
mechanics (see (3) in the next section).                         ing is to recover u on the entire image domain Ω

16                                              NOTICES   OF THE    AMS                               VOLUME 50, NUMBER 1```
```as faithfully as possible from the available data u0
on D .
From Shannon’s Theorem to Variational
Inpainting
Interpolation is a classical topic in approximation
theory, numerical analysis, and signal and image
processing. Successful interpolants include poly-
nomials, harmonic waves, radially symmetric func-
tions, finite elements, splines, and wavelets. Despite
the diversity of the literature, there exists one most
widely recognized result due to Shannon, known
as Shannon’s Sampling Theorem.
Theorem (Shannon’s Theorem). If a signal u(t) is           Figure 1. TV inpainting for the error concealment of a blurry
band-limited within (−ω, ω), then                          image.

∞                      
π          ω                      to specify the conditional probability p(u0 |u) .
u(t) =        u n     sinc     t −n .
n=−∞     ω          π                      Finally, in the Bayesian framework, Shannon’s
“then” statement is replaced, as indicated earlier,
That is, if an analog signal u(t) (with finite en-     by the Maximum A Posteriori (MAP) optimization
ergy or, equivalently, in L2 (R ) ) does not contain any
given by Bayes’s formula:
high frequencies, then it can be perfectly interpo-
lated from its properly sampled discrete sequence          (1)        max p(u|u0 ) = p(u0 |u)p(u)/p(u0 ).
u
u0 [n] = u(nπ /ω) (where ω/π is known as the
Nyquist frequency).                                        (It is equivalent to maximizing the product of the
All interpolation problems share this “if-then”        prior model and the data model, since the de-
structure. “If” specifies the space where the target       nominator is a fixed normalization constant once
signal u is sought, while “then” gives the recon-          u0 is given.) To summarize, Bayesian inpainting
struction or interpolation procedure based on the          means finding the most probable image given its
discrete samples (or, more generally, any partial          incomplete and possibly distorted observation.
information about the signal).                                 The variational approach resembles the Bayesian
Unfortunately, for most real applications in sig-      methodology, but now everything is expressed
nal and image processing, one cannot expect a              deterministically. The Bayesian prior model p(u)
closed-form formula as clean as Shannon’s. This is         becomes the specification of the regularity of
due to at least two factors. First, in vision analysis     an image u , while the data model p(u0 |u) now
and communication, signals like images are in-             measures how well the observation u0 fits if the
trinsically not band-limited because of the presence
original image is indeed u. Regularity is enforced
of edges (or Heaviside-type singularities). Second,
through “energy” functionals:
            for example, the
for most real applications, the given incomplete
Sobolev norm E[u] = Ω |∇u|2 dx , the total variation
data are often noisy and become blurred during the
(TV) model E[u] = Ω |Du| of Rudin, Osher, and
imaging and transmission processes. Therefore,
Fatemi, and the Mumford-Shah free-boundary
in the situation of Shannon’s Theorem, we are deal-
model E[u, Γ ] = Ω\Γ |∇u|2 dx + βH 1 (Γ ) , where H 1
ing with a class of “bad” signals u with “unreliable”
denotes the one-dimensional Hausdorff measure.
samples u0 .
The quality of data fitting u → u0 is often judged
Naturally, for image inpainting, both the “if”
and “then” statements in Shannon’s Theorem need            by an error measure E[u0 |u] . For instance, the least
to be modeled carefully. It turns out that there are       square measure prevails in the literature due to the
two powerful and interdependent frameworks that            genericity of Gaussian-type noise and the Central
1
can carry out this task: one is the variational            Limit Theorem: E[u0 |u] = |D| D (T u − u0 )2 dx,
method, and the other is the Bayesian frame-               where D is the domain on which u0 has been
work .                                                 sampled or measured, |D| is its area (or cardinal-
In the Bayesian approach the “if” statement            ity for the discrete case), and T denotes any linear
specifies both the so-called prior model and the           or nonlinear image processor (such as blurring and
data model. The prior model specifies how images           diffusion). In this variational setting, Shannon’s
are distributed a priori or, equivalently, which           “then” statement becomes a constrained opti-
images occur more frequently than others. Proba-           mization problem:
bilistically, it specifies the prior probability p(u) .
min E[u]           over all u such that E[u0 |u] ≤ σ 2 .
Let u0 denote the incomplete data that are ob-
served, measured, or sampled. Then the second part         Here σ 2 denotes the variance of the white noise,
of “if” is to model how u0 is generated from u or          which is assumed to be known by proper statistical

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```Figure 2. Mumford-Shah inpainting for text removal.

estimators. Equivalently, the model solves the fol-            The data model is explicitly given by
lowing unconstrained problem using Lagrange mul-                                        
1
tipliers (e.g., Chambolle and Lions):                          (4)     E[u0 |u, D] =       (Ku − u0 )2 dx.
|D| D
(2)             min E[u] + λE[u0 |u].
u                                             Therefore, from the variational point of view, the
Generally, λ expresses the balance between regu-               quality of an inpainting model crucially depends
larity and fitting. In summary, variational inpaint-           on the prior model or the regularity
     energy E[u] .
ing searches for the most “regular” image that best                The TV prior model E[u] = Ω |Du| was first in-
fits the given observation.                                    troduced into image processing by Rudin, Osher,
The Bayesian approach is more universal in the              Fatemi in . Unlike the Sobolev image model
sense of allowing general statistical prior and data           E2 [u] = Ω |∇u|2 dx , the TV model recognizes one
models, and it is powerful for restoring both arti-            of the most important vision features, the “edges”.
ficial images and natural images (or textures). But            For example, for a cartoon image u showing the
to learn the prior model and the data model is                 night sky (u = 0 ) with a full bright moon (u = 1 ),
usually quite expensive. The variational approach              the
 Sobolev energy blows up, while the TV energy
is ideal for dealing with regularity and geometry
Ω |Du| equals the perimeter of the moon, which
and tends to work best for man-made indoor and                 is finite. Therefore, in combination with the data
outdoor scenes and images with low textures. The               model (4), the variational TV inpainting model
two approaches (1) and (2) can be at least formally            minimizes
unified under Gibbs’s formula in statistical me-                                                
chanics:                                                       (5) Etv [u|u0 , D] = α   |Du| + λ (Ku − u0 )2 dx.
Ω             D
(3)   E[·] ∝ −β log p(·),        or p(·) ∝ e−E[·]/β ,
The admissible space is BV(Ω) , the Banach space
where β = kT is the product of the Boltzmann con-              of all functions with bounded variation. It is very
stant and temperature, and ∝ means equality up                 similar to the celebrated TV restoration model of
to a multiplicative or additive constant. (However,            Rudin, Osher, and Fatemi . In fact, the beauty
the definability of a rigorous probability measure             and power of the model exactly lie in the provision
over “all” images is highly nontrivial because of the          of a unified framework for denoising, deblurring,
multiscale nature of images. Recent efforts can be             and image reconstruction from incomplete data.
found in the work of Mumford and Gidas.)                       Figure 1 displays the computational output of the
Variational Inpainting Based on Geometric Image                model applied to a blurry image with simulated ran-
Models                                                         dom packet loss due to the transmission failure of
In a typical image inpainting problem, u0 denotes              a network.
the observed or measured incomplete portion of                    The second well-known prior model is the ob-
a clean “good” image u on the entire image domain              ject-edge model of Mumford and Shah . The
Ω . A simplified but already very powerful data                edge set Γ is now explicitly singled out, unlike in
model in various digital applications is blurring fol-         the TV model, and an image u is understood as a
lowed by noise degradation and spatial restriction:            combination of both the geometric feature Γ and
u0       = (Ku + n)D ,                        the piecewise smooth “objects” ui on all the con-
D
nected components Ωi of Ω \ Γ . Thus in both the
where K is a continuous blurring kernel, often                 Bayesian and the variational languages, the prior
assumed to be linear or even shift-invariant, and n            model consists of two parts (applying (3) for the
is an additive white noise field assumed to be close           transition between probability and “energy”):
to Gaussian for simplicity. The information u0 |Ω\D
is missing or inaccessible. The goal of inpainting is                     p(u, Γ ) = p(u|Γ )p(Γ )       and
to reconstruct u as faithfully as possible from u0 D .                    E[u, Γ ] = E[u|Γ ] + E[Γ ].

18                                             NOTICES   OF THE   AMS                               VOLUME 50, NUMBER 1```
```In the Mumford-Shah model the edge regularity is
specified by E[Γ ] = H 1 (Γ ) , the one-dimensional
Hausdorff measure, or in most computational ap-
plications, E[Γ ] = length(Γ ) , assuming that Γ is Lip-
schitz. The smoothness of the “objects” is naturally
characterized
        by the ordinary Sobolev norm:
E[u|Γ ] = Ω\Γ |∇u|2 dx . Therefore, in combination
with the data model (4), the variational inpainting
model based on the Mumford-Shah prior is given
by                              
(6)   inf Ems [u, Γ |u0 , D] = α      |∇u|2 dx
u,Γ                          Ω\Γ

+ βH 1 (Γ ) + λ (Ku − u0 )2 dx.        Figure 3. Smooth inpainting by the Mumford-
D
Shah-Euler model.
Figure 2 shows one application of this model for
and Shen , and as expected it improves the TV
text removal. Notice that edges are preserved and
inpainting model.
smooth regions remain smooth.
Similarly, the Mumford-Shah image model Ems
Numerous applications have demonstrated that,
can be improved by replacing the length energy by
for classical applications in denoising, deblurring,
Euler’s elastica energy:
and segmentation, both the TV and the Mumford-                                          
Shah models perform sufficiently well even by the                      Emse [u, Γ ] = α   |∇u|2 dx + e[Γ ].
high standard of human vision. But inpainting does                                     Ω\Γ
have special characteristics. We have demonstrated
in , ,  that for large-scale inpainting prob-        This was first applied to image inpainting by Ese-
lems, high-order image models which incorporate               doglu and Shen . Figure 3 shows one example of
the curvature information become necessary for                applying this image prior model to the inpainting
more faithful visual effects.                                 of an occluded disk. Both the TV and Mumford-Shah
The key to high-order geometric image models               inpainting models would complete the interpola-
is Euler’s elastica curve model:                              tion with a straight-line edge and introduce visible
                                          corners as a result. The elastica model restores
the smooth boundary.
e[γ] = (a + bκ 2 ) ds, a, b > 0,
γ                                              The improved performance of curvature-based
models comes at a price in terms of both theory
where κ denotes the scalar curvature. Birkhoff and
and computation. The existence and uniqueness of
de Boor called it the “nonlinear spline” model in
the TV and Mumford-Shah inpainting models can
approximation theory. It was first introduced into
be studied in a fashion similar to the classical
computer vision by Mumford. Unlike straight
restoration and segmentation problems. But theo-
lines (for which b = 0 ), the elastica model allows
retical study on high-order models is only begin-
smooth curves because of the curvature term,                  ning. The difficulty lies in the involvement of the
which is important for computer vision and com-               second-order geometric feature of curvature and
puter graphics.                                               in the identification of a proper function space to
By imposing the elastica energy on each indi-             study the models. Secondly, in terms of computa-
vidual level line of u (at least symbolically or by           tion, the calculus of variation on the curvature
assuming that u is regular enough), we obtain the             term leads to fourth-order highly nonlinear PDEs,
so-called elastica image model:                               whose fast and efficient numerical solution im-
∞                                        poses a tremendous challenge.
Eel [u] =     e[u ≡ λ] dλ                               We conclude this section with a brief discussion
−∞
∞                                       of computation, especially for the TV and Mumford-
(7)               =         (a + bκ 2 ) ds dλ                 Shah inpaintings.
−∞ u≡λ                                       For the TV inpainting model Etv , the Euler-
= (a + bκ 2 )|∇u| dx.                       Lagrange equation is formally (or assuming that u
Ω                                      is in the Sobolev space W 1,1 ) given by
In the last integrand the curvature is given by                                ∇u
κ = ∇ · [∇u/|∇u|] . (Notice that in the absence of            (8)      −∇ ·        + µK ∗ χD (Ku − u0 ) = 0.
|∇u|
the curvature term, the above formula is exactly the
co-area formula for smooth functions (e.g., Giusti).          Here K ∗ denotes the adjoint of the linear blurring
This elastica prior model was first studied for in-           kernel K, the multiplier χD (x) is the indicator of D ,
painting by Masnou and Morel, and by Chan, Kang,              and µ = 2λ/α . The boundary condition along ∂Ω

JANUARY 2003                                             NOTICES   OF THE   AMS                                         19```
```is Neumann adiabatic to eliminate any boundary              Caselles, and Ballester, and by Bertalmio, Bertozzi,
contribution during the integration-by-parts process.       and Sapiro. In particular, it has been interestingly
This nonlinear PDE can be solved iteratively by             found in the latter paper that the earlier PDE model
the freezing technique: if u(n) denotes the current         by Ballester, Bertalmio, Caselles, Sapiro, and Verdera
inpainting at step n, then the updated inpainting           is closely related to the stream function–vorticity
u(n+1) solves the linearized PDE                            equation in fluid dynamics.

∇u(n+1)                                          Variational Level Set Image Segmentation
−∇ ·               + µK ∗ χD (Ku(n+1) − u0 ) = 0.
|∇u(n) |                                         Images are the proper 2-D projections of the 3-D
world containing various objects. To successfully
In practice the intermediate diffusivity coeffi-            reconstruct the 3-D world, at least approximately,
cient
      1/|∇u(n) | is often modified to                   the first crucial step is to identify the regions in im-
1/ |∇u(n) |2 + *2 for some small conditioning pa-           ages that correspond to individual objects. This is
rameter * or by the mandatory ceiling and floor-            the well-known problem of image segmentation. It
ing between * and 1/* . The convergence of such             has broad applications in a variety of important
algorithms has been well studied in the literature          fields such as computer vision and medical image
(e.g., Chambolle and Lions, and Dobson and Vogel).          processing.
There are also many other possible techniques in               Denote by u0 an observed image on a 2-D Lipschitz
the literature for solving (8) (e.g., Vogel and Oman,       open and bounded domain Ω . Segmentation means
and Chan, Mulet, and Golub). We need only to re-            finding a visually meaningful edge set Γ that leads
late (8) to the conventional TV restoration case.           to a complete partition of Ω . Each connected com-
The computation of the Mumford-Shah inpaint-             ponent Ωi of Ω \ Γ should correspond to at most one
ing model is also very interesting. For inpainting,         real physical object or pattern in our 3-D world,
unlike segmentation, one’s direct interest is only in       for example, the white matter in brain images or the
u, not in Γ. Such understanding makes the Γ-conver-         abnormal tissues in organs. In some applications,
gence approximation theory perfect for inpainting.          one is interested also in the clean image patches
According to Ambrosio and Tortorelli, by introduc-          ui on each Ωi of the segmentation, since u0 is often
ing an edge signature function
       z(x) ∈ [0, 1] , x ∈ Ω,   noisy.
and having E[u|Γ ] = α Ω\Γ |∇u|2 dx replaced by               Therefore, there are two crucial ingredients in
E[u|z] = α Ω z 2 |∇u|2 dx , one can approximate the         the mathematical modeling and computation of
length energy in the Mumford-Shah model by a qua-           the segmentation problem. The first is how to
dratic integral in z (up to a constant multiplier):         formulate a model that appropriately combines
                                           the effects of both the edge set Γ and its segmented
*|∇z|2    (z − 1)2                       regions {Ωi , i = 1, 2, · · · } . The other is to find
E* [z] = β              +             dx, *  1.
Ω      2         2*                          the most efficient way to represent the geometry
of both the edge set and the regions and to repre-
Thus the Mumford-Shah inpainting model is ap-               sent the segmentation model as a result. This of
proximated by                                               course reflects the general philosophy in the
introduction.
E* [u, z|u0 , D] = E[u|z] + E* [z] + λE[u0 |u, D],          In the variational PDE approach, these two issues
have found good answers in the literature: for
which is a quadratic integral in both u and z ! It leads    the first, the celebrated segmentation model of Mum-
to a coupled system of linear elliptic-type PDEs in         ford and Shah  and for the second, the level-set
both u and the edge signature z , which can be              representation technology of Osher and Sethian .
solved efficiently using any numerical elliptic solver.     In what follows we detail our recent efforts in ad-
The example in Figure 2 was computed by this                vancing the application of the level-set technology to
scheme.                                                     various Mumford-Shah-related image segmentation
Finally, we mention some of the major applica-           models. Much of the work can be found in our pa-
tions of the inpainting and geometric image inter-          pers (e.g., , , ,  and many more on our
polation models developed above. These include              group homepage ) and also in related works by
digital zooming, primal-sketch-based perceptual             Yezzi, Tsai, and Willsky; Paragios and Deriche; Zhu
image coding, error concealment for wireless image          and Yuille; and Cohen, Bardinet, and Ayache , .
transmission, and progressive disocclusion in com-             We start with a novel active-contour model whose
puter vision , , . Extensions to color or more     formulation is independent of intensity edges de-
general hyperspectral images and nonflat image              fined by the gradients, in contrast to most con-
features (i.e., ones that live on Riemannian mani-          ventional ones in the literature. We then explain how
folds) are also currently being studied in the liter-       this model can be efficiently computed based on the
ature. Other approaches to the inpainting problem           multiphase level-set method. In the second part we
can be found in the papers by Bertalmio, Sapiro,            extend these results to the level-set formulation and

20                                          NOTICES   OF THE   AMS                               VOLUME 50, NUMBER 1```
```computation of the general Mumford-Shah
segmentation model for piecewise-smooth
images. In the last part we present our re-
cent work on extending the previous mod-
els to logical operations on multichannel
image objects.
Active Contours without Edges and
Multiphase Level Sets
The active contour is a powerful tool in
image and vision analysis for boundary de-
tection and object segmentation. The key
idea is to evolve a curve so that it eventu-
ally stops along the object edges of the given
image u0 . The curve evolution is controlled                 Figure 4. Top: Detection of a simulated minefield by our new active-
by two sorts of energies: the internal energy                contour model. Bottom: Segmentation of an MRI brain image. Notice
defining the regularity of the curve and the                 that the interior boundaries are automatically detected.
external energy determined by the given
image u0 . The latter is often called the
feature-driven energy.
In almost all classical active-contour
models, the feature-driven energies rely
heavily on the gradient feature |∇u0 | or on
its smoothed version |∇Gσ ∗ u0 | , where
Gσ denotes a Gaussian kernel with a small
variance σ . They work well for detecting
gradient-defined edges but fail for more
general classes of edges such as the bound-
ary of a nebula in some astronomical images
or the top image in Figure 4.
Our new model, active contours without
edges, first introduced in , is independent               Figure 5. Left: Two curves given by φ1 = 0 and φ2 = 0 partition the
of the gradient information and therefore can                domain into four regions based on indicator vector (sign(φ1 ),sign(φ2 )) .
handle more general types of edges. The                      Right: Three curves given by φ1 = 0 , φ2 = 0 , and φ3 = 0 partition the
model is to minimize the energy                              domain into eight regions based on the triple (sign(φ1 ),sign(φ2 ),
sign(φ3 )) .

E2 [c1 , c2 , Γ |u0 ] =          |u0 (x) − c1 |2 dx                                    
int(Γ )
                                                       E2 [c1 , c2 , φ|u0 ] =    |u0 (x) − c1 |2 H(φ) dx
Ω
(9)         +           |u0 (x) − c2 |2 dx + ν|Γ |,                                                          
ext(Γ )
+ |u0 (x)−c2 |2 (1−H(φ)) dx+ν               |∇H(φ)| dx.
Ω                                     Ω
where ν denotes a given positive weight, the c ’s are
unknown constants, int(Γ ) and ext(Γ ) denote the                    Minimizing E2 [c1 , c2 , φ|u0 ] with respect to c1 , c2 ,
interior and exterior of Γ, and |Γ | is its length. The              and φ leads to the Euler-Lagrange equation:
subscript 2 in E2 indicates that it deals with two-
phase images, i.e., ones whose “objects” can be                                ∂φ            ∇φ 
completely indexed by the interior and exterior                                   = δ(φ) νdiv
∂t             |∇φ|
of Γ.                                                                                                                      
In the level-set formulation of Osher and                                                    − |u0 − c1 |2 + |u0 − c2 |2 ,
Sethian , Γ is embedded as the zero-level set                                      
u0 (x)H(φ(x)) dx
{φ = 0} of a Lipschitz continuous function                                  c1 (t) =   Ω                   ,
φ : Ω → R . Consequently, {φ > 0} and {φ < 0} de-                                           Ω H(φ(x)) dx

fine the interior Ω+ and exterior Ω− of the curve.                                         u0 (x)(1 − H(φ(x))) dx
c2 (t) =   Ω                         ,
(The level-set approach is computationally superior                                         Ω (1 − H(φ(x))) dx
to other curve representations, because it lets one
directly work on a fixed rectangular grid and it                     with a suitable initial guess φ(0, x) = φ0 (x) . In nu-
allows automatic topological changes such as merg-                   merical implementations the Heaviside function
ing and breaking.) Denote by H the 1-dimensional                     H(z) is often regularized by some Hε (z) in C 1 (R)
Heaviside function: H(z) = 1 if z ≥ 0 and 0 if z < 0 .               that converges as ε → 0 to H(z) in some suitable
Then the energy in our model becomes                                 sense. As a result, the Dirac function δ(z) in the last

JANUARY 2003                                                    NOTICES   OF THE   AMS                                               21```
```active-contour model is insufficient, and we need
to introduce multiple level-set functions. Therefore,
we have generalized the above framework to multi-
phase active contours or, equivalently, the piece-
wise-constant Mumford-Shah segmentation with
multiphase regions:

inf Ems [u, Γ |u0 ]
u,Γ

(10)             =         |u0 − ci |2 dx + ν|Γ |.
i   Ωi

Here the Ωi ’s denote the connected components of
Ω \ Γ , and u = ci on Ωi . Notice that Γ can now be a
general set of edge curves, including for example
the T-junction class.
Generally, consider m level-set functions
φi : Ω → R . The union of the zero-level sets of the
φi represents the edges in the segmented image.
Using these m level-set functions, one can define
up to n = 2m phases, which form a disjoint and
complete partitioning of Ω . Therefore, each point
x ∈ Ω belongs to one and only one phase. In par-
ticular, there is no vacuum or overlap among the
phases. This is an important advantage compared
with the classical multiphase representation, where
a level-set function is associated to each phase and
more level-set functions are needed as a result.
Figure 6. The original and segmented images
Figure 5 shows two typical examples of multiphase
(top row), and the final four segments (the rest).
partitioning corresponding to m = 2 and m = 3 .
equation is regularized to δε (z) = Hε (z) . We have        We now illustrate the multiphase level-set ap-
discovered in  that a carefully designed approx-       proach through the example of n = 4 and m = 2 .
imation scheme can even allow interior contours to        Let c = (c11 , c10 , c01 , c00 ) denote a constant vector
emerge, a challenging task for most conventional          and Φ = (φ1 , φ2 ) the two-phase level-set vector.
algorithms. Also notice that the length term in the       Then we are looking for an ideal image u in the form
energy has led to the mean-curvature motion.              of
The model performs as an active contour in the
class of piecewise-constant images taking only two        u = c11 H(φ1 )H(φ2 ) + c10 H(φ1 )(1−H(φ2 ))
values; it looks for a two-phase segmentation of a            + c01 (1−H(φ1 ))H(φ2 ) + c00 (1−H(φ1 ))(1−H(φ2 )).
given image. The internal energy is defined by the
length, while the external energy is independent of          The Mumford-Shah segmentation energy be-
the gradient |∇u0 | . Defining the segmented image        comes
by u(x) = c1 H(φ(x)) + c2 (1 − H(φ(x))) , we realize                           
that the energy model is exactly the Mumford-Shah             E4 [c, Φ|u0 ] =     |u0 (x) − c11 |2 H(φ1 )H(φ2 ) dx
Ω
segmentation model  restricted to the class of                
piecewise-constant images. However, our model                  +      |u0 (x) − c10 |2 H(φ1 )(1 − H(φ2 )) dx
Ω
was initially developed from the active-contour                   
point of view.                                            (11) +      |u0 (x) − c01 |2 (1 − H(φ1 ))H(φ2 ) dx
Two typical numerical outputs of the model are                 Ω

displayed in Figure 4. The top row shows that our              +      |u0 (x) − c00 |2 (1 − H(φ1 ))(1 − H(φ2 )) dx
model can segment and detect objects without                       Ω
                       
clear gradient edges. The bottom one shows that
+ν       |∇H(φ1 )| dx + ν        |∇H(φ2 )| dx.
it can also capture complicated boundaries and                         Ω                      Ω
interior contours.
For more complicated situations where multiple         Its minimization leads to the Euler-Lagrange equa-
objects occlude each other and multiphase edges           tions. First, with Φ fixed, the c minimizer can be
such as T-junctions emerge, the above two-phase           explicitly worked out as before:

22                                        NOTICES   OF THE   AMS                                  VOLUME 50, NUMBER 1```
```cij (t) = average of u0 on
{(2i − 1)φ1 > 0, (2j − 1)φ2 > 0},
i, j = 0, 1.
In turn, this new c information leads to the Euler-
Lagrange equations for Φ :

∂φ1              ∇φ 
1
= δ(φ1 ) νdiv
∂t                 |∇φ1 |
                          
− (u0 − c11 )2 − (u0 − c01 )2 H(φ2 )
                                      
− (u0 − c10 )2 − (u0 − c00 )2 (1 − H(φ2 )) ,           Figure 7. An example of texture segmentation
(at increasing times).

∂φ2              ∇φ 
= δ(φ2 ) νdiv
2                                   now show how to carry out the model based on the
∂t                 |∇φ2 |                                   multiphase level-set approach . As before, we
                          
− (u0 − c11 )2 − (u0 − c01 )2 H(φ1 )                   start with the two-phase situation where a single
                                      
level-set function φ is sufficient, followed by the
− (u0 − c10 )2 − (u0 − c00 )2 (1 − H(φ1 )) .
more general multiphase case.
Notice that the equations are governed both by the               In the two-phase situation, the ideal image u is
mean curvatures and by jumps of the data-energy               segmented to u± by the level-set function φ:
terms across the boundary.
Figure 6 shows an application of the model to                     u(x) = u+ (x)H(φ(x)) + u− (x)(1 − H(φ(x))).
the medical analysis of a brain image. Displayed are
the final segmented image and its associated four             We assume that both u+ and u− are C 1 functions
phases. Our model successfully identifies and seg-
up to the boundary {φ = 0} . Substituting this ex-
ments the white and the gray matters.
Recently the above models and algorithms have              pression into (12), we obtain

been extended to multichannel, volumetric, and
E[u+ , u− , φ|u0 ] =    |u+ − u0 |2 H(φ) dx
texture images (e.g., Chan, Sandberg, and Vese ).                                  Ω

Let us give a little more detail about texture
segmentation from our work. Texture images are                    +     |u− u0 |2 (1 − H(φ)) dx
Ω
general images of natural scenes, such as grass-              (13)
lands, beaches, rocks, mountains, and human body                            
tissues. They typically carry certain coherent struc-                +µ         |∇u+ |2 H(φ) dx
Ω
tures in scales, orientations, and local frequencies.                                                      
To segment texture images using the above mod-                       +µ         |∇u− |2 (1 − H(φ)) dx + ν       |∇H(φ)|.
Ω                               Ω
els, we first apply Gabor’s filters to extract these
coherent structures. The filter responses create a
First, with φ fixed, the variation on
new vectorial (or multichannel) feature image in
the form of U(x) = (uα (x), uβ (x), · · · , uγ (x)) , where   E[u+ , u− , φ|u0 ] leads to the two Euler-Lagrange
the Greek letters stand for the filter signatures, and        equations for u± separately:
typically each takes a value of (scale, orientation,
local frequency). We then apply the vectorial active-                       u± − u0 = µu±        on ± φ > 0,
contour-without-edges model to the segmentation               (14)                  ±
∂u
of U. Figure 7 shows one typical example.                                            =0           on {φ = 0}.
∂n
Piecewise-Smooth Mumford-Shah Segmentation
The most general Mumford-Shah piecewise-smooth                (Here ± takes either of the values + and − , but uni-
segmentation  is defined by                               formly across the formula.) They act as denoising

operators on the homogeneous regions only. No-
(12) inf Ems [u, Γ |u0 ] =    |u − u0 |2 dx
u,Γ                 Ω                                 tice that no smoothing is done across the bound-

ary {φ = 0} , which is very important in image
+µ       |∇u|2 dx + ν|Γ |,
Ω\Γ                          analysis.
Next, keeping the functions u+ and u− fixed and
where µ and ν are positive parameters. It allows
the segmented “objects” to have smoothly varying              minimizing E[u+ , u− , φ|u0 ] with respect to φ, we
intensities instead of being strictly constant. We            obtain the motion of the zero-level set:

JANUARY 2003                                             NOTICES   OF THE   AMS                                            23```
```As in the previous section, there are cases where
the boundaries forming a complete partition of the
image cannot be represented by a single level-set
function. Then one has to turn to the multi-phase
approach. In our papers, thanks to the planar Four-
Color Theorem, we have been able to conclude that
two level-set functions are sufficient for all multi-
phase partition problems.
By the Four-Color Theorem one can color all the
regions in a partition using only four colors, so that
any two adjacent regions are color distinguishable.
Identifying a phase with one color, we see that two
level-set functions φ1 and φ2 are sufficient to pro-
duce four “colors”: {±φ1 > 0 , ±φ2 > 0} . There-
Figure 8. Numerical result from the piecewise-smooth             fore, they can completely segment a general image
Mumford-Shah level-set algorithm with one             with a multiphase boundary set Γ given by {φ1 = 0}
level-set function.          or {φ2 = 0}. As before, we do not have the prob-
lems of “overlapping” or “vacuum” as in the works
A
A 1
1
A2
A2
by Zhao, Chan, Merriman, and Osher. Note that in
this formulation, generally each “color” can still
have many isolated components. Therefore, the
segmentation is complete only after one applies an
extra step of the well-known topological processor
for finding the connected components of an open
set.
In this four-phase formulation, the ideal image
Figure 9. A synthetic example of an object in
u is segmented into four disjoint but complete
two different channels. Notice that the lower
parts u±± , each defined by one of the four phases:
left corner of A1 and the upper corner of
A2 are missing.                         {±φ1 > 0, ±φ2 > 0}.

Λ1∪Λ2              Λ1∩Λ2              Λ1∪¬Λ2            Overall, by using the Heaviside function, we obtain
the following synthesis formula:
u =u++ H(φ1 )H(φ2 ) + u+− H(φ1 )(1 − H(φ2 ))
+ u−+ (1 − H(φ1 ))H(φ2 )
+ u−− (1 − H(φ1 ))(1 − H(φ2 )),

for all x ∈ Ω. We can express the energy function
Figure 10. Different logical combinations for           of u and Φ = (φ1 , φ2 ) in a similar way and derive
the sample image: the union, the intersection,           the corresponding Euler-Lagrange equations.
and the differentiation.              Notice the remarkable feature of this single
                                       model, which includes both the original energy
∂φ                 ∇φ
= δ(φ) ν∇            − (|u+ − u0 |2                  formulation and the elliptic and evolutionary PDEs:
∂t                |∇φ|                                   it naturally combines all three image processors—
active contour, segmentation, and denoising.
+ µ|∇u+ |2 − |u− u0 |2 − µ|∇u− |2 ) ,
Logic Operators for Multichannel Image
Segmentation
with some initial guess φ(t = 0, x) . The above equa-       In a multichannel image u(x) = (u1 (x), u2 (x),
tion is actually computed at least near a narrow            · · · , un (x)) , a single physical object can leave
band of the zero-level set. As a result, computa-           different traces in different channels. For example,
tionally we have to continuously extend both u+ and         Figure 9 shows a two-channel image containing
a triangle that is, however, incomplete in each
u− from their original domains {±φ > 0} to a suit-
individual channel. For this example, most con-
able neighborhood of the zero-level set {φ = 0} .
ventional segmentation models for multichannel
Figure 8 displays an application of the model in as-        images (e.g., Guichard, Sapiro, Zhu and Yuille)
tronomical image analysis. Although the nebula              would output the complete triangle, i.e., the union
itself does not seem to be a smooth object, the             of both channels. The union is just one of the
piecewise-smooth model can still correctly cap-             several possible logical operations for multichan-
ture the main features.                                     nel images. For example, the intersection and the

24                                               NOTICES   OF THE   AMS                              VOLUME 50, NUMBER 1```
```Truth table for the two-channel case
z1in z2in z1out z2out A1 ∪ A2 A1 ∩ A2                   A1 ∩ ¬A2
differentiation are also very common in applica-                   x inside Γ            1      1       0       0         1                1     1
tions, as illustrated in Figure 10.                                (or x ∈ Ω+ )          1      0       0       0         0                1     1
In this section we outline our recent efforts in                                      0      1       0       0         0                1     0
developing logical segmentation schemes for multi-                                       0      0       0       0         0                0     1

channel images based on the active-contour-                        x outside Γ           0      0       1       1         1                1     0

(or x ∈ Ω− )
without-edges model .                                                                0      0       1       0         1                0     1
0      0       0       1         1                0     0
First, we define two logical variables to encode
0      0       0       0         0                0     0
the information inside and outside the contour Γ
separately for each channel i:                                    Table 2. The truth table for two channels. Notice that inside Γ


1, if x is inside Γ and not on           “true” is represented by 0 . It is designed so as to encourage
ziin (u0i , x, Γ ) =     the object,                           the contour to enclose the targeted logical object at a lower

0, otherwise;                            energy cost.
                                             is because in practice, as mentioned above, the z ’s

1 if x is outside Γ and on
are approximated and take continuous values. For
zi (u0 , x, Γ ) =
out i
the object,

0 otherwise.                                 example, possible interpolants for the union and
intersection are

Such different treatments are motivated by the en-                   fA1 ∪A2 (x) = z1in (x)z2in (x)
ergy minimization formulation. Intuitively speak-                                                                   
ing, in order for the active contour Γ to evolve and                              + 1 − (1 − z1out (x))(1 − z2out (x)) ,
eventually capture the exact boundary of the tar-                                      
geted logical object, the energy should be designed                  fA1 ∩A2 (x) =1 − (1 − z1in (x))(1 − z2in (x))
so that both partial capture and overcapture lead                                    
to high energies (corresponding to ziout = 1 and                                  + z1out (x)z2out (x).
ziin = 1 separately). Imagine that the target object
is tumor tissue: then in terms of decision theory,                The square roots are taken to keep the functions
over and partial captures correspond respectively                 of the same order as the original scalar models. It
to false alarms and misses. Both are to be penal-                 is straightforward to extend the two-channel case
ized.                                                             to more general n-channel ones.
The energy functional E for the logical objective
In practice we do not have precise information
function f can be expressed by the level set func-
of “the object” to be segmented. One possible way
tion φ. Generally, as just shown above, the objec-
to approximate ziin and ziout is based on the inte-
tive function can be separated into two parts,
rior (Ω+ ) and exterior (Ω− ) averages ci± in channel i:
f = f (z1in , z1out , · · · , znin , znout )
|u0i (x) − ci+ |2
ziin (u0i , x, Γ )   =                           ,                  = fin (z1in , · · · , znin ) + fout (z1out , · · · , znout ).
maxy∈Ω+ |u0i (y) − ci+ |2

for x ∈ Ω+, and                                                   The energy functional is then defined by

|u0i (x) − ci− |2                       E[φ|c + , c − ] = µlength(φ = 0)
ziout (u0i , x, Γ ) =                             ,                          
maxy∈Ω− |u0i (y) − ci− |2
+ λ [fin (z1in , · · · , znin )H(φ)
Ω
for x ∈ Ω−.                                                                      + fout (z1out , · · · , znout )(1 − H(φ))] dx.
The desired truth table can then be described
using the ziin ’s and ziout ’s. Table 2 shows three ex-           Here each c ± = (c1± , · · · , cn± ) is in fact a multi-
amples of logical operations for the two-channel                  channel vector. The associated Euler-Lagrange equa-
case. Notice that “true” is represented by 0 inside               tion is similar to the scalar model:
Γ. The method is designed so as to encourage en-                                        ∇φ 
ergy minimization when the contour tries to cap-                     ∂φ
= δ(φ) µdiv
ture the targeted object inside. Also note that the                  ∂t                 |∇φ|
“ziin ” terms and the “ziout ” terms play asymmetric                                                                                
but complementary roles. For example, the union                        − λ fin (z1in , · · · , znin ) − fout (z1out , · · · , znout ) ,
A1 ∪ A2 corresponds to the union of the “in” terms
and the intersection of the “out” terms. Similarly,               with suitable boundary conditions as before. Even
the intersection A1 ∩ A2 corresponds to the inter-                though the form often looks complicated for a typ-
section of the “in” terms and the union of the “out”              ical application, its implementation is very similar
terms.                                                            to that of the scalar model.
We then design continuous objective functions                    Numerical results support our above efforts. Fig-
to smoothly interpolate the binary truth table. This              ure 9 shows two different occlusions of a triangle.

JANUARY 2003                                                 NOTICES   OF THE   AMS                                                                  25```
```We are able to success-           of Variations, Appl. Math. Sci., vol. 147, Springer-Ver-
fully recover the union,          lag, 2001.
the intersection, and the      T. F. CHAN, S.-H. KANG, and J. SHEN, Euler’s elastica and
differentiation of the ob-        curvature based inpaintings, SIAM J. Appl. Math. (2002),
jects in Figure 10 using          in press.
our model. In Figure 11        T. F. CHAN, B. SANDBERG, and L. VESE, Active contours with-
out edges for vector-valued images, J. Visual Comm.
we have a two-channel
Image Rep. 11 (1999), 130–41.
image of the brain. In
 T. F. CHAN and J. SHEN, Mathematical models for local
one channel we have a
nontexture inpaintings, SIAM J. Appl. Math. 62 (2001),
“tumor” with some                 1019–43.
noise, while the other         T. F. CHAN and L. A. VESE, Active contours without
channel is clear. The im-         edges, IEEE Trans. Image Process. 10 (2001), 266–77.
ages are not registered.       L. COHEN, Avoiding local minima for deformable curves
We want to find                   in image analysis, Proc. 3rd. Internat. Conf. on Curves
A1 ∩ ¬A2 so that the              and Surfaces (Chamonix-Mont Blanc, 1996), Vol. 1, (A.
tumor can be observed.            Le Méhauté, C. Rabut, and L. L. Schumaker, eds.), Van-
Figure 11. Region-based logical
This happens to be a              derbilt Univ. Press, Nashville, TN, 1997, pp. 77–84.
model on a medical image. In the first                                      L. COHEN, E. BARDINET, and N. AYACHE, Surface recon-
very complicated exam-
channel, A1, the noisy image has a                                         struction using active contour models, Proc. SPIE Conf.
ple, as there are a lot of
“brain tumor”, while channel A2 does                                          on Geometric Methods in Computer Vision, SPIE, Belling-
features and textures.
not. The goal is to spot the tumor                                        ham, WA, 1993.
However, the model
that is in channel A1 but not in A2, i.e.,                                  M. G. CRANDALL, H. ISHII, and P. L. LIONS, User’s guide to
finds the tumor suc-
the differentiation A1 ∩ ¬A2 . In the                                       viscosity solutions of second order partial linear dif-
cessfully.
right-hand column we observe that                                           ferential equations, Bull. Amer. Math. Soc. (N.S.) 27
the tumor has been successfully                                          (1992), 1–67.
captured.                                    S. ESEDOGLU and J. SHEN, Digital inpainting based on the
Mumford-Shah-Euler image model, European J. Appl.
Math. 13 (2002), 353–70.
Conclusion
 S. GEMAN and D. GEMAN, Stochastic relaxation, Gibbs
In this article we have discussed some recent develop-                distributions, and the Bayesian restoration of images,
ments in one successful approach to mathematical im-                  IEEE Trans. Pattern Anal. Machine Intell. 6 (1984),
age and vision analysis, the variational PDE method.                  721–41.
Besides the inpainting and segmentation problems dis-              UCLA Imagers homepage, http://www.math.ucla.
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space theory, geometric processing of curves and sur-                 demic Press, 1998.
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rich literature of numerical analysis and computational
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PDEs. This subject shows that mathematics has a key role           L. RUDIN, S. OSHER, and E. FATEMI, Nonlinear total vari-
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ther theoretical study on the variational and PDE mod-             B. SANDBERG and T. F. CHAN, Logic operations for
els developed in recent years, more intrinsic integration             active contours on multi-channel images, UCLA
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Acknowledgments                                                    G. SAPIRO, Geometric Partial Differential Equations
Jackie Shen thanks his former advisors Gilbert Strang and             and Image Processing, Cambridge University Press,
Stan Osher for their constant support and inspiration.                2001.
 L. A. VESE and T. F. CHAN, A multiphase level set
framework for image segmentation using the Mumford
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