Journal ArticleDOI

# A comparison of three total variation based texture extraction models

01 Jun 2007-Journal of Visual Communication and Image Representation (Academic Press, Inc.)-Vol. 18, Iss: 3, pp 240-252

TL;DR: This paper qualitatively compares three recently proposed models for signal/image texture extraction based on total variation minimization: the Meyer, Vese-Osher (VO), and TV-L^1[12,38,2-4,29-31] models.

AbstractThis paper qualitatively compares three recently proposed models for signal/image texture extraction based on total variation minimization: the Meyer [27], Vese-Osher (VO) [35], and TV-L^1[12,38,2-4,29-31] models We formulate discrete versions of these models as second-order cone programs (SOCPs) which can be solved efficiently by interior-point methods Our experiments with these models on 1D oscillating signals and 2D images reveal their differences: the Meyer model tends to extract oscillation patterns in the input, the TV-L^1 model performs a strict multiscale decomposition, and the Vese-Osher model has properties falling in between the other two models

Topics: Image texture (54%)

### 1 Introduction

• Let f be an observed image that contains texture and/or noise.
• Texture is characterized as repeated and meaningful structure of small patterns.
• Noise is characterized as uncorrelated random patterns.
• The rest of an image, which is called cartoon, contains object hues and sharp edges .

### 1.1 The spaces BV and G

• In image processing, the space BV and the total variation semi-norm were first used by Rudin, Osher, and Fatemi [33] to remove noise from images.
• The ROF model is the precursor to a large number of image processing models having a similar form.

### 1.3 Second-order cone programming

• Since a one-dimensional second-order cone corresponds to a semi-infinite ray, SOCPs can accommodate nonnegative variables.
• In fact if all cones are onedimensional, then the above SOCP is just a standard form linear program.
• As is the case for linear programs, SOCPs can be solved in polynomial time by interior point methods.
• This is the approach that the authors take to solve the TV-based cartoon-texture decomposition models in this paper.

### 2.2.3 The Vese-Osher (VO) model

• This is equivalent to solving the residual-free version (45) below.
• The authors chose to solve the latter in their numerical tests because using a large λ in (44) makes it difficult to numerically solve its SOCP accurately.

### 3 Numerical results

• Similar artifacts can also be found in the results Figures 2 (h )-(j) of the VO model, but the differences are that the VO model generated u's that have a block-like structure and thus v's with more complicated patterns.
• In Figure 2 (h), most of the signal in the second and third section was extracted from u, leaving very little signal near the boundary of these signal parts.
• In short, the VO model performed like an approximation of Meyer's model but with certain features closer to those of the TV-L 1 model.

### Example 2:

• This fingerprint has slightly inhomogeneous brightness because the background near the center of the finger is whiter than the rest.
• The authors believe that the inhomogeneity like this is not helpful to the recognition and comparison of fingerprints so should better be corrected.
• The authors can observe in Figures 4 (a ) and (b) that their cartoon parts are close to each other, but slightly different from the cartoon in Figure 4 (c).
• The VO and the TV-L 1 models gave us more satisfactory results than Meyer's model.
• Compared to the parameters used in the three models for decomposing noiseless images in Example 3, the parameters used in the Meyer and VO models in this set of tests were changed due to the increase in the G-norm of the texture/noise part v that resulted from adding noise.

### 4 Conclusion

• The authors have computationally studied three total variation based models with discrete inputs: the Meyer, VO, and TV-L 1 models.
• The authors tested these models using a variety of 1D sig- nals and 2D images to reveal their differences in decomposing inputs into their cartoon and oscillating/small-scale/texture parts.
• The Meyer model tends to capture the pattern of the oscillations in the input, which makes it well-suited to applications such as fingerprint image processing.
• On the other hand, the TV-L 1 model decomposes the input into two parts according to the geometric scales of the components in the input, independent of the signal intensities, one part containing large-scale components and the other containing smallscale ones.
• These results agree with those in [9] , which compares the ROF, Meyer, and TV-L 1 models.

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A
Comparison
of Three Total Variation Based
Texture Extraction Models
?
Wotao Yin
a
, Donald Goldfarb
b
, Stanley Osher
c
a
Rice University, Department of Computational and Applied Mathematics, 6100
Main St, MS-134, Houston, TX 77005, USA
b
Columbia University, Department of Industrial Engineering and Operations
Research, 500 West 120th St, Mudd 313, New York, NY 10027, USA
c
UCLA Mathematics Department, Box 951555, Los Angeles, CA 90095, USA
Abstract
This
pap
er qualitatively compares three recently proposed models for signal/image
texture extraction based on total variation minimization: the Meyer [27], Vese-Osher
(VO) [35], and TV-L
1
[12,38,2–4,29–31] models. We formulate discrete versions of
these models as second-order cone programs (SOCPs) which can be solved eﬃciently
by interior-point methods. Our experiments with these models on 1D oscillating
signals and 2D images reveal their diﬀerences: the Meyer model tends to extract
oscillation patterns in the input, the TV-L
1
model performs a strict multiscale
decomposition, and the Vese-Osher model has properties falling in between the
other two models.
Key words: image decomposition, texture extraction, feature selection, total
variation, variational imaging, second-order cone programming, interior-point
method
1
In
troduction
Let f be an observed image that contains texture and/or noise. Texture is
characterized as repeated and meaningful structure of small patterns. Noise is
?
Researc
h
supported by NSF Grants DMS-01-04282, DNS-03-12222 and ACI-03-
21917, ONR Grants N00014-03-1-0514 and N00014-03-0071, and DOE Grant GE-
FG01-92ER-25126.
Email addresses: wotao.yin@rice.edu (Wotao Yin), goldfarb@columbia.edu
(Donald Goldfarb), sjo@math.ucla.edu (Stanley Osher).
Preprint submitted to Elsevier 21 January 2007

c
haracterized as uncorrelated random patterns. The rest of an image, which
is called cartoon, contains object hues and sharp edges (boundaries). Thus an
image f can be decomposed as f = u+v, where u represents image cartoon and
v is texture and/or noise. A general way to obtain this decomposition using
the variational approach is to solve the problem min {T V (u) | kufk
B
σ},
where T V (u) denotes the total variation of u and k · k
B
is a norm (or semi-
norm). The total variation of u, which is deﬁned below in Subsection 1.1, is
minimized to regularize u while keeping edges like object boundaries of f in
u (i.e., while allowing discontinuities in u). The ﬁdelity term ku fk
B
σ
forces u to be close to f.
1.1 The spaces BV and G
The Banach space BV of functions of bounded variation is important in image
processing because such functions are allowed to have discontinuities and hence
keep edges. This can be seen from its deﬁnition as follows.
Let u L
1
, and deﬁne [39]
T V (u) := sup
Z
u div(~g) dx :
~g C
1
0
(R
n
; R
n
),
|~g(x)| 1 for all x R
n
as the total variation of u, where |·| denotes the Euclidean norm. Also, u BV
if kuk
BV
:= kuk
L
1
+ T V (u) < . In the above deﬁnition, ~g C
1
0
(R
n
; R
n
),
the set of continuously diﬀerentiable vector-valued functions, serves as a test
set for u. If u is in the Sobolev spaces W
1,1
or H
1
, it follows from integration
by parts that T V (u) is equal to
R
|∇u|, where u is the weak derivative
of u. However, the use of test functions to deﬁne T V (u) allows u to have
discontinuities. Therefore, BV is larger than W
1,1
and H
1
. Equipped with the
k · k
BV
-norm, BV is a Banach space.
BV (Ω) with being a bounded open domain is deﬁned analogously to BV
with L
1
and C
1
0
(R
n
; R
n
) replaced by L
1
(Ω) and C
1
0
(Ω; R
n
), respectively.
Next, we deﬁne the space G [27]. Let G denote the Banach space consisting
of all generalized functions v(x) deﬁned on R
n
, which can be written as
v = div(~g), ~g = [g
i
]
i=1,...,n
L
(R
n
; R
n
). (1)
Its norm kvk
G
is deﬁned as the inﬁmum of all L
norms of the functions
|~g(x)| over all decompositions (1) of f . In short, kvk
G
= inf{k(|~g(x)|) k
L
:
v = div(~g)}. G is the dual of the closed subspace BV of BV , where BV :=
{u BV : |∇f| L
1
} [27]. We note that ﬁnite diﬀerence approximations to
2

functions
in BV and BV are the same. For the deﬁnition and properties of
G(Ω), see [5].
An immediate result of the above deﬁnitions is that
Z
u v =
Z
u ·~g =
Z
u ·~g T V (u)kvk
G
, (2)
holds for any u BV with compact support and v G. We say (u, v) is an
extremal pair if (2) holds with equality.
In image processing, the space BV and the total variation semi-norm were
ﬁrst used by Rudin, Osher, and Fatemi [33] to remove noise from images.
Speciﬁcally, their model obtains a cleaner image u BV of a noisy image f
by letting u be the minimizer of T V (u)+λkufk
2
L
2
, in which the regularizing
term T V (u) tends to reduce the oscillations in u and the data ﬁdelity term
ku fk
L
2
tends to keep u close to f.
The ROF model is the precursor to a large number of image processing models
having a similar form. Among the recent total variation-based cartoon-texture
the G-norm deﬁned above, Vese and Osher [35] approximated the G-norm by
the div(L
p
)-norm, Osher, Sole and Vese [32] proposed using the H
1
-norm,
Lieu and Vese [26] proposed using the more general H
s
-norm, and Le and
Vese [24] and Garnett, Le, Meyer and Vese [18] proposed using the homo-
geneous Besov space
˙
B
s
p,q
, 2 < s < 0, 1 p, q , extending Meyer’s
˙
B
1
,
, to model the oscillation component of an image. In addition, Chan and
Esedoglu [12] and Yin, Goldfarb and Osher [38] used the L
1
-norm together
with total variation, following the earlier work by Alliney [2–4] and Nikolova
[29–31].
1.2 Three cartoon-texture decomposition models
In this subsection we present three cartoon-texture decomposition models that
are based on the minimization of total variation. We suggest that readers in-
terested in the theoretical analysis of these models read the referenced works
mentioned below and in the introduction. Although the analysis of the ex-
istence and uniqueness of solutions and duality/conjugacy is not within the
scope of our discussion, in Section 3 we relate the diﬀerences among the image
results from these models to the distinguished properties of the three ﬁdelity
terms: kf uk
L
1
, kf uk
G
, and its approximation by Vese and Osher.
In the rest of the paper, we assume the input image f has compact support
contained in a bounded convex open set Ω. In our tests, is an open square.
3

1.2.1
The TV-L
1
model
In [2–4,30,31,12,37] the square of the L
2
norm of f u in the ﬁdelity term
in the original ROF model (min{T V (u) + λkf uk
2
L
2
}) is replaced by the L
1
norm of f u, which yields the following problem:
Constraint model: min
uBV
{
Z
|∇u|, s.t.
Z
|f u| σ}, (3)
Lagrangian model: min
uBV
Z
|∇u| + λ
Z
|f u|. (4)
The above constrained minimization problem (3) is equivalent to its La-
grangian relaxed form (4), where λ is the Lagrange multiplier of the constraint
R
|f u|. The two problems have the same solution if λ is chosen equal to the
optimal value of the dual variable corresponding to the constraint in the con-
strained problem. Given σ or λ, we can calculate the other value by solving the
corresponding problem. The same result also holds for Meyer’s model below.
We chose to solve the Lagrangian relaxed version (4), rather than the con-
straint version (3), in our numerical experiments because several researchers
[12,37] have established the relationship between λ and the scale of f u
. For
example, for the unit disk signal 1
B(0,r)
centered at origin and with radius r,
f u
= 1
B(0,r)
for 0 < λ < 2/r while f u
vanishes for λ > 2/r. Although
this model appears to be simpler than Meyer’s model and the Vese-Osher
model below, it has recently been shown to have very interesting properties
like morphological invariance and texture extraction by scale [12,37]. These
properties are important in various applications in biomedical engineering and
computer vision such as background correction [36], face recognition [14,15],
and brain MR image registration [13]. In Section 3, we demonstrate the ability
of the TV-L
1
model to separate out features of a certain scale in an image.
In addition to the SOCP approach that we use in the paper to solve (4)
numerically, the graph-based approaches [17,11] were recently demonstrated
very eﬃcient in solving an approximate version of (4).
1.2.2 Meyer’s model
To extract cartoon u in the space BV and texture and/or noise v as an oscil-
lating function, Meyer [27] proposed the following model:
Constraint version: inf
uBV
{
Z
|∇u|, s.t. kf uk
G
σ}, (5)
Lagrangian version: inf
uBV
Z
|∇u| + λkf uk
G
. (6)
4

As w
e have pointed out in Section 1.1, G is the dual space of BV, a sub-
space of BV . So G is closely connected to BV . Meyer gave a few examples,
including the one shown at the end of next paragraph, in [27] illustrating the
appropriateness of modeling oscillating patterns by functions in G.
Unfortunately, it is not possible to write down Euler-Lagrange equations for
the Lagrangian form of Meyer’s model (6), and hence, use a straightforward
partial diﬀerential equation method to solve it. Alternatively, several models
[5,6,32,35] have been proposed to solve (6) approximately. The Vese-Osher
model [35] described in the next subsection approximates ||(|~g(x)|) k
L
by
||(|~g(x)|) k
L
p
, with 1 p < . The Osher-Sole-Vese model [32] replaces kvk
G
by the Hilbert functional kvk
2
H
1
. The more recent A
2
BC model [5,7,6] is
inspired by Chambolle’s projection algorithm [10] and minimizes T V (u) +
λkf u vk
2
L
2
for (u, v) BV × {v G : kvk
G
µ}. Similar projection
algorithms proposed in [9] and [8] are also used to approximately solve (4) and
(6). Recently, Kindermann and Osher [21] showed that (6) is equivalent to a
minimax problem and proposed a numerical method to solve this saddle-point
problem. Other numerical approaches based on the dual representation of the
G-norm are introduced in [16] by Chung, Le, Lieu, Tanushev, and Vese, [25]
by Lieu, and [23] by Le, Lieu, and Vese. In [34], Starck, Elad, and Donoho
use sparse basis pursuit to achieve a similar decomposition. In Section 2, we
present SOCP-based optimization models to solve both (5) and (6) exactly
(i.e., without any approximation or regularization applied to the non-smooth
terms
R
|∇u| and kvk
G
except for the use of ﬁnite diﬀerences). In contrast to
our choice for the TV-L
1
model, we chose to solve (5) with speciﬁed σ’s in our
numerical experiments because setting an upper bound on kf uk
G
is more
meaningful than penalizing kf uk
G
. The following example demonstrates
that kvk
G
is inversely proportional to the oscillation of v: let v(t) = cos(xt),
which has stronger oscillations for larger t; one can show kvk
G
= 1/t because
cos(xt) =
d(
1
t
sin(xt))
dx
and k
1
t
sin(xt)k
L
=
1
/t. Therefore, to separate a signal
with oscillations stronger than a speciﬁc level from f, it is more straightforward
to solve the constrained problem (5).
To calculate the G-norm of a function f alone, one can choose to solve an
SOCP or use the dual method by Kindermann, Osher and Xu [22]. The authors
of the latter work exploit (2) to develop a level-set based iterative method.
1.2.3 The Vese-Osher model
Motivated by the deﬁnition of the L
norm of |~g(x)| as the limit
k(|~g|)k
L
= lim
p→∞
k(|~g|)k
L
p
, (7)
5

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