A comparison of three total variation based texture extraction models
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
Latent Fingerprint Enhancement Using Sparse Representation
Minal Tandale,Shraddha Panbude +1 more
TL;DR: The work proposes the method of enhancement using sparse representation, where total variation model will be used for latent fingerprint image, where image is disintegrated into cartoon and texture components and second sparse enhancement algorithm is implemented on texture component of the image.
Proceedings ArticleDOI
Multi-scale decomposition for remote sensing image processing
Zhou Lijia,Qian Zhibo,Xu Guanlei +2 more
TL;DR: Based on image gradients, an alternative image multi-scale decomposition approach for remote sensing image processing is derived through solving a Poisson equation in this article, which outperforms the other methods such as bilateral filter (BLF) and weighted least squares based multiscale decomposition (WLSMD) in edge preserving image decomposition via experiments.
Book ChapterDOI
Content Adaptive Constraint Based Image Upsampling
TL;DR: A novel image upsampling method within a two-stage framework to reconstruct different image content to outperforms the state-of-the-art approaches, based on subjective and objective evaluations.
Study of Image Local Scale Structure Using Nonlinear Diffusion
TL;DR: A new definition of local scale is proposed which can be interpreted with a clear geometrical meaning and applied in general image analysis and the potential applications of total variation model in retinal fundus image analysis is explored.
Proceedings ArticleDOI
A Regularization Approach for InSAR and Optical Data Fusion
TL;DR: This paper defines the regularized elevation in the framework of Markov random fields (MRF) and derive a smoothness prior that both preserves sharp boundaries (based on total variation minimization) and is driven by the structures present in the optical image.
References
More filters
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.
Related Papers (5)
Nonlinear total variation based noise removal algorithms
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing
Luminita A. Vese,Stanley Osher +1 more