A comparison of three total variation based texture extraction models
TLDR
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.About:
This article is published in Journal of Visual Communication and Image Representation.The article was published on 2007-06-01 and is currently open access. It has received 68 citations till now. The article focuses on the topics: Image texture.read more
Figures
Fig. 5. Example 4 and 5, cartoon-texture decomposition: left halves - cartoon, right halves - texture/noise. 
Fig. 1. Example 1: 1D signal decomposition 
Table 1 Mosek termination measures 
Fig. 3. Inputs: (a) original 117×117 fingerprint, (b) original 512×512 Barbara, (c) a 256×256 part of original Barbara, (d) a 256×256 part of noisy Barbara (std.=20), (e) original 256× 256 4texture. 
Fig. 2. Example 1: 1D signal decomposition (continue) 
Fig. 4. Examples 2 and 3, cartoon-texture decomposition results: left halves - cartoon, right halves - texture.
Citations
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Journal ArticleDOI
Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
TL;DR: In this paper, the authors proposed simple and extremely efficient methods for solving the basis pursuit problem, which is used in compressed sensing, using Bregman iterative regularization, and they gave a very accurate solution after solving only a very small number of instances of the unconstrained problem.
Journal ArticleDOI
Just Noticeable Difference for Images With Decomposition Model for Separating Edge and Textured Regions
TL;DR: In this letter, an enhanced pixel domain JND model with a new algorithm for CM estimation is proposed, and the proposed one shows its advantages brought by the better EM and TM estimation.
Journal ArticleDOI
Efficient Schemes for Total Variation Minimization Under Constraints in Image Processing
TL;DR: New fast algorithms to minimize total variation and more generally $l^1$-norms under a general convex constraint and a recent advance in convex optimization proposed by Yurii Nesterov are presented.
Journal ArticleDOI
Fast Cartoon + Texture Image Filters
TL;DR: This paper converts the linear model, which reduces to a low-pass/high-pass filter pair, into a nonlinear filter pair involving the total variation, which retains both the essential features of Meyer's models and the simplicity and rapidity of thelinear model.
Journal ArticleDOI
The Total Variation Regularized $L^1$ Model for Multiscale Decomposition
TL;DR: It is shown that the images produced by this model can be formed from the minimizers of a sequence of decoupled geometry sub-problems, and that the TV-L1 model is able to separate image features according to their scales.
References
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Book ChapterDOI
Image decomposition application to SAR images
TL;DR: An algorithm to split an image into a sum u + v of a bounded variation component and a component containing the textures and the noise is constructed and it is shown how the u component can be used in nontextured SAR image restoration.
Journal ArticleDOI
Background correction for cDNA microarray images using the TV+L1 model
TL;DR: Experimental results demonstrate that the TV+L1 model gives the restored intensity that is closer to the true data than morphological opening, and can serve an important role in the preprocessing of cDNA microarray data.
ReportDOI
The Total Variation Regularized L1 Model for Multiscale Decomposition
TL;DR: In this paper, a total variation regularization model with an L1 fidelity term (TV-L1) is proposed for decomposing an image into features of different scales. But the model is not suitable for image segmentation.
Image decomposition: application to textured images and SAR images
TL;DR: A new algorithm to split an image f into a component u belonging to BV and a component v made of textures and noise of the initial image is presented and it is shown how the v component can be used to classify textured images, and on the other hand, how the u component could be used in SAR image restoration.
Journal ArticleDOI
Recursive median filters of increasing order: a variational approach
TL;DR: It is shown that a good approximation to the minima of such a functional can be obtained, for any signal, by means of successive applications of recursive median filters of increasing order.
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