Image decomposition, image restoration, and texture modeling using total variation minimization and the H/sup -1/ norm

Osher, Stanley; Solé, A.; Vese, Luminita A.

doi:10.1109/icip.2003.1247055

Cited by 203 publications

(330 citation statements)

References 11 publications

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“…The success of this penalty stems from the fact that it allows discontinuous solutions and hence preserves edges while Þltering high-frequency oscillations due to noise. Several other methods are derived from the original ROF model by Meyer [77], Osher [82], Vese and Osher [109].…”

Section: Variational Formulations and Diffusion þLteringmentioning

confidence: 99%

From Local Kernel to Nonlocal Multiple-Model Image Denoising

et al. 2009

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We review the evolution of the nonparametric regression modeling in imaging from the local Nadaraya-Watson kernel estimate to the nonlocal means and further to transform-domain Þltering based on nonlocal block-matching. The considered methods are classiÞed mainly according to two main features: local/nonlocal and pointwise/multipoint. Here nonlocal is an alternative to local, and multipoint is an alternative to pointwise. These alternatives, though obvious simpliÞcations, allow to impose a fruitful and transparent classiÞcation of the basic ideas in the advanced techniques. Within this framework, we introduce a novel single-and multiplemodel transform domain nonlocal approach. The Block Matching and 3-D Filtering (BM3D) algorithm, which is currently one of the best performing denoising algorithms, is treated as a special case of the latter approach.

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Section: Variational Formulations and Diffusion þLteringmentioning

confidence: 99%

From Local Kernel to Nonlocal Multiple-Model Image Denoising

et al. 2009

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“…As in many recent publications [15,16,17,18,19,20], we adopt the TV regularizer to handle the ill-posed nature of the problem of inferring x. This amounts to computing the herein termed TV estimate, which is given by…”

Section: Problem Formulationmentioning

confidence: 99%

“…Total variation (TV) regularization was introduced by Rudin, Osher, and Fatemi in [15] and has become popular in recent years [15,16,17,18,19,20]. Recently, the range of application of TV-based methods has been successfully extended to inpainting, blind deconvolution, and processing of vector-valued images (e.g., color).…”

Section: Introductionmentioning

confidence: 99%

Total Variation-Based Image Deconvolution: a Majorization-Minimization Approach

Bioucas-Dias

Figueiredo

Oliveira

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

162

190

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The total variation regularizer is well suited to piecewise smooth images. If we add the fact that these regularizers are convex, we have, perhaps, the reason for the resurgence of interest on TV-based approaches to inverse problems. This paper proposes a new TV-based algorithm for image deconvolution, under the assumptions of linear observations and additive white Gaussian noise. To compute the TV estimate, we propose a majorization-minimization approach, which consists in replacing a difficult optimization problem by a sequence of simpler ones, by relying on convexity arguments. The resulting algorithm has O(N ) computational complexity, for finite support convolutional kernels. In a comparison with state-of-the-art methods, the proposed algorithm either outperforms or equals them, with similar computational complexity.

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“…Most of the algorithms dealing with the problem ( [6], [7], [8] to name a few) try to minimize an energy term that penalize oscillatory features in the supposedly piecewise smooth image and vice-versa. The algorithm in [8] decomposes the image by applying low-pass (cartoon) and high-pass (texture) filters.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Texture Recognition Using Decorrelation Stretching

Kalfon¹,

Porat²

2013

IJFCC

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Abstract-Texture features have always been a key attribute in image recognition and classification. In this paper we propose two pre-processing methods for enhancing the performance of widely used color texture recognition methods. In the first approach we propose decorrelation stretching for color enhancement, which is known to improve the interpretability of color images. The second method employs Cartoon-Texture decomposition for sharpening the texture component of the image. We show that both methods improve the classification accuracy by 7% and 4% respectively when applied to images prior to extracting auto and cross-correlation features. Our conclusion is that the proposed a p p r o a c h could be helpful in machine vision tasks.

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Image decomposition, image restoration, and texture modeling using total variation minimization and the H/sup -1/ norm

Cited by 203 publications

References 11 publications

From Local Kernel to Nonlocal Multiple-Model Image Denoising

From Local Kernel to Nonlocal Multiple-Model Image Denoising

Total Variation-Based Image Deconvolution: a Majorization-Minimization Approach

A New Approach to Texture Recognition Using Decorrelation Stretching

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