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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Journal ArticleDOI
A Variational Approach to Remove Outliers and Impulse Noise
TL;DR: The variational method furnishes a new framework for the processing of data corrupted with outliers and different kinds of impulse noise and is accurate and stable, as demonstrated by the experiments.
Journal ArticleDOI
Image Decomposition and Restoration Using Total Variation Minimization and the H1
TL;DR: A new model for image restoration and image decomposition into cartoon and texture is proposed, based on the total variation minimization of Rudin, Osher, and Fatemi, and on oscillatory functions, which follows results of Meyer.
Journal ArticleDOI
Total variation models for variable lighting face recognition
TL;DR: The logarithmic total variation (LTV) model is presented, which has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition.
Journal ArticleDOI
Practical, Unified, Motion and Missing Data Treatment in Degraded Video
TL;DR: The idea is to use MCMC to solve the resulting problem articulated under a Bayesian framework, but to deploy purely deterministic mechanisms for dealing with the solution, which results in a relatively fast implementation that unifies many of the pixel-by-pixel schemes previously described in the literature.
Journal ArticleDOI
Image Decomposition into a Bounded Variation Component and an Oscillating Component
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, inspired from a recent work of Y. Meyer.
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