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Analysis of a new variational model for image multiplicative denoising
Journal of Inequalities and Applicationsvolume 2013, Article number: 568 (2013)
In this paper, we study the mathematical properties of a new variational model for image multiplicative noise removal. Some important properties of the model, including the lower semicontinuity, the differential property, the convergence and regularization property, are established for the first time. The existence and uniqueness of a solution for the problem as well as a comparison principle have also been established.
MSC: Primary 49J40; 65K10; secondary 94A08.
Consider the following variational problem:
where is an open bounded open set with Lipschitz-regular boundary ∂ Ω, λ is a constant, is a given function, ϕ is an even function from to R having the linear growth
where , M is a positive constant and its value is determined by the size of an image.
The total variation has been introduced in computer vision first by Rudin, Osher and Fatemi  as a regularizing criterion for solving inverse problems. It has proved to be very efficient for regularizing images without smoothing the boundaries of the objects. A variety of variational methods have been proposed for imaging processing over the last decades, and the main variational approaches devoted to multiplicative noises include the RLO model  proposed by Rudin et al., the AA model  by Aubert and Aujol, and the JY model  by Jin and Yang.
Variational problem (1) is a multiplicative noise removal model. This model was first obtained via the MAP estimator . It is specifically devoted to the denoising of images corrupted by a gamma noise. This model cannot yield good denoising effectiveness, but it can significantly avoid or reduce ‘edge blur’ and ‘step effect’.
From a mathematical point of view, most of the existing variational models for denoising have been studied extensively [2–4, 6]. For example, for a very informative discussion of the use of total variation regularization in the field of image processing, see the introduction of . In , Aubert and Aujol proved the existence of a minimizer for a variational model and studied the associated evolution problem, for which they derived existence and uniqueness results for the solution. Moreover, they proved the convergence of an implicit iterative scheme to compute the solution. Jin and Yang in  established the existence and uniqueness of a weak solution for the associated evolution equations of the JY model, and showed that the solution of the evolution equation converges weakly in BV and strongly in to the minimizer as . For model (1), the initial boundary value problem of the partial differential equation for the model is derived and discreted numerically. The experiments in  showed that the quality of the images restored by the model is excellent. However, in order to further study and apply model (1), further rigorous work is needed to investigate the mathematical properties of the model.
The main goal of this paper is thus to further study the mathematical properties of model (1). The study is conducted in the space of functions with bounded variations (BV). We first establish some important mathematical properties for the function ϕ in the model including the lower semicontinuity, the differential property, the convergence and regularization of its mollification . Then it is easy to prove the existence and uniqueness of a solution for the model under appropriate assumptions. Furthermore, a comparison principle is obtained.
The paper is organized as follows. Some preliminaries are given in the next section. In Section 3, some properties about the function ϕ are obtained. Some new results for variational model (1) such as the existence, uniqueness and comparison results are derived in Section 4. Finally, a conclusion is given in Section 5.
In this paper, we use the following classical distributional spaces. For the convenience of readers, we here recall some basic notations and facts, and for details we refer the readers to the works of Aubert-Kornprobst in .
Let denote the Lebesgue measure of a measurable set E in , let denote the Hausdorff measure of dimension , and let and be the standard notations for the Sobolev and Lebesgue spaces, respectively, let be the set of functions in with compact support in Ω.
Definition 2.1 If for all functions , , the formula
holds, is called the distribution gradient of u, u is called a bounded variation function, and the total variation of Du on Ω is defined as follows:
Definition 2.2 The space of bounded variation functions, , is defined as follows:
It should be addressed here that the endowed with the norm is a Banach space.
With regard to the compactness in , we state the following theorem.
Theorem 2.1 ()
Assume that is a bounded sequence in . Then there exists a subsequence and a function such that as in .
Definition 2.3 The approximate upper limit and the approximate lower limit , respectively, are defined by
where is the ball of center x and radius ρ. When is different from , we define the jump set as follows:
After choosing a normal () pointing toward the largest value of u, we recall the following decompositions (see  for more details):
where ∇u is the density of the absolutely continuous part of Du with respect to the Lebesgue measure, is the singular part, is the Cantor part, and is the jump part, is the Hausdorff measure restricted to the set .
Let be the usual mollifier with compact support and . If , define its mollification in . That is,
Using the standard properties of mollifiers, we have the following properties.
If and , then and in and if , then in .
If for all x, then for all .
If , then .
If , then .
Proof Using the standard properties of mollifiers, we can easily prove the above results. □
3 Some properties about ϕ
By the definition of ϕ in (2), it is easy to see that ϕ belongs to and ϕ is Lipschitz continuous with the gradient
Furthermore, it can be proved that
Theorem 3.1 If , where is the mollifier, then
and is convex.
uniformly on compact subsets of as .
for all .
Proof (1) Since ϕ is continuous and convex, is clearly smooth and convex in (cf. Theorem 6 of Appendix C in ).
According to (4), for all , we have(6)
For a fixed point x, by the definition of , we have from (6) that
Now and , we choose and note that ϕ is uniformly continuous on W. Thus the limit (6) holds uniformly for . Consequently, the calculation above implies uniformly on V.
(3) Let . The convexity of implies that , so . Further, we have
For the first term on the right-hand side, we consider two possible cases.
Case 1. . Since , it implies . Noting that the function is increasing on , we obtain that . So the first term on the right-hand side is nonnegative.
Case 2. . Since , , and so . In terms of the convexity of ϕ, we have
Noting that , we get
For the second term on the right-hand side, noting that , we have
Therefore, . □
Remark Since is convex, increasing on with linear growth at infinity, and , for , we can get the Lebesgue decomposition of the measure :
Since , for all , we have
The lower semicontinuity of the functional ϕ with respect to convergence in , which is one of the most important properties of BV functions, is given in the next theorem below.
Theorem 3.2 Assume that and in as , then
Proof Let be such that . Then
Taking the supremum over all such v, we have
Thus (8) follows on combining (7) and (9). □
Theorem 3.3 (Regularization)
Suppose . If are the mollified functions described in Section 2 (where u is extended to be 0 outside Ω if necessary), then
Proof Since as in from Theorem 2.2, we have, by Theorem 3.2, the inequality
and so it remains only to prove a verse inequality.
For , by the method of the proof in Theorem A.2 of , we can obtain
Suppose and , then, by Theorem 2.2,
Since , by Theorem 2.2, we get that . On taking the supremum over all such v and noting (10), we get that
4 Some facts for the variational problem
Consider the variational problem. Assume that with , find a function such that
For the existence of solutions for minimization problem (12), we have obtained the existence result in the BV space (see ), namely, for with , problem (12) has at least one solution satisfying .
We find that, under appropriate assumptions, we can prove the existence of a Lipschitz solution for problem (12) as detailed below.
Theorem 4.1 (Existence of a Lipschitz solution)
Suppose that Ω is a bounded open domain in and with . Then problem (12) has at least one Lipschitz solution if for any , there exist two planes in , and such that
the slopes of these planes are uniformly bounded, independent of x, by a constant K. That is, for all .
Proof Consider the approximation of problem (12), that is, replacing by . By the general elliptic theory (see ), there exists a minimizer for each . The proof in Section 10 of  shows that has a uniform gradient bound which is not greater than K, then, by applying to a subsequence of as , we get a Lipschitz function u, and the rest of the proof is straightforward. □
Theorem 4.2 (Uniqueness)
Let be in , then problem (12) has at most one solution such that .
Proof Letting , we have and . We thus deduce that if , then τ is strictly convex. Using (7) and noting that is convex, increasing on with linear growth at infinity, we obtain the uniqueness of a minimizer. □
Theorem 4.3 (Comparison principle)
Let and be in with and . Assume that in Ω. We denote by (resp., ) a solution of (12) for (resp., ). Then we have .
Proof Let . From Theorem 4 in , we know that and exist. Since is a minimizer with data (), we have
Writing , we easily get that
Since , we thus deduce that has a zero Lebesgue measure; i.e., a.e. in Ω. □
Remark The above result agrees with observation in image processing. Suppose that and are two images with noise. and are from and after denoising by some method, respectively. If the image is dimer than at almost every pixel, then is not naturally lighter than .
In this paper, we study the mathematical properties of an important variational model proposed in our recent work  for removing multiplicative noise. For the first time, many important properties of the model, including the lower semicontinuity, the differential property, the convergence and regularization property are established and proved. The well-posedness of the underlying mathematical problem for the model has also been established. The comparison principle of solutions for the model has also been obtained.
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The first author is supported partly by the National Natural Science Foundation of China (11071266), and partly by the Chinese Scholarship Council during the author’s visit to Curtin University of Technology. The second author is supported by the Australian Research Council.
The authors declare that they have no competing interests.
All authors completed the paper together. All authors read and approved the final manuscript.