A comparison of three total variation based texture extraction models
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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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The TVL1 model : a geometric point of view Une étude géométrique du modèle TVL1
TL;DR: In this paper, the authors investigate the geometrical behavior of the TVL1 model used in image processing, by making use of the notion of Cheeger sets, and propose a new and efficient numerical scheme to apply the model to digital images.
Proceedings ArticleDOI
A new pansharpening method based on cartoon+texture decomposition
Meysam Lotfi,Hassan Ghassemian +1 more
TL;DR: A cartoon+texture decomposition is used to discriminate between necessary and unnecessary pixels to inject details and results show it preserves more spectral features with less spatial distortion.
Patent
Seismic texture extraction and enhancement method based on total variation seismic data decomposition model
Yang Ning,Chen Ting +1 more
TL;DR: In this paper, a seismic texture extraction and enhancement method based on a total variation seismic data decomposition model is proposed, which comprises the steps: seismic data is acquired on the field, a post-stack seismic section is decomposed, and weighting Gaussian smoothing is performed on a texture component n(t) for obtaining a complete seismic texture section instead of a gray-level projection section with decreased latitude.
Book ChapterDOI
Computing the Local Continuity Order of Optical Flow Using Fractional Variational Method
TL;DR: Numerical results show that the Horn-Schunck type prior involving the n + *** order differentiation for 0 < *** < 1 and an integer n is suitable for accurate optical flow computation.
Dissertation
Cartoon-Residual Image Decompositions with Application in Fingerprint Recognition
TL;DR: A generalized cartoon-residual decomposition algorithm that features a high-dimensional set of continuous parameters to obtain a highly flexible algorithm such that choosing a decomposition model can be reformulated as a parameter selection problem.
References
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Journal ArticleDOI
Nonlinear total variation based noise removal algorithms
TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.
Journal ArticleDOI
Second-order cone programming
Farid Alizadeh,Donald Goldfarb +1 more
TL;DR: SOCP formulations are given for four examples: the convex quadratically constrained quadratic programming (QCQP) problem, problems involving fractional quadRatic functions, and many of the problems presented in the survey paper of Vandenberghe and Boyd as examples of SDPs can in fact be formulated as SOCPs and should be solved as such.
MonographDOI
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
TL;DR: It turns out that this mathematics involves new properties of various Besov-type function spaces and leads to many deep results, including new generalizations of famous Gagliardo-Nirenberg and Poincare inequalities.
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