Fast Image Restoration Methods for Impulse and Gaussian Noises Removal

Huang, Yu-Mei; Ng, Michael K.; Wen, You-Wei

doi:10.1109/lsp.2009.2016835

Cited by 64 publications

(64 citation statements)

References 15 publications

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“…The restoration step can for instance rely on filtering [6], variationnal principles [9,5], on patch redundancy [12,7] or combine the two last approaches via dictionnary learning [11], to name just a few. The most recent approaches are often dedicated to the more realistic mixture of Gaussian and impulse noise [8,12,7,11].…”

Section: Introductionmentioning

confidence: 99%

PARIGI: a Patch-based Approach to Remove Impulse-Gaussian Noise from Images

Delon¹,

Desolneux²,

Guillemot³

2016

Image Processing on Line

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In this paper, we present an implementation of the PARIGI method that addresses the problem of the restoration of images affected by impulse noise or by a mixture of Gaussian and impulse noise. The method relies on a patch-based approach, which requires careful choices for both the distance between patches and for the statistical estimator of the original patch. Experiments are performed in the case of pure impulse noise and in the case of a mixture of Gaussian and impulse noise. Source CodeThe source code and the code documentation are available as supplementary material. The documentation is included in the archive. Basic compilation and usage instructions are included in the README.txt file. All those files and the online demo are accessible at the IPOL web page of this article 1 .

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Section: Introductionmentioning

confidence: 99%

PARIGI: a Patch-based Approach to Remove Impulse-Gaussian Noise from Images

Delon¹,

Desolneux²,

Guillemot³

2016

Image Processing on Line

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“…The suppression of the disturbances introduced by the impulsive noise is indispensable for the success of further stages of the image processing pipeline [7][8][9][10][11][12] and, therefore, we present a novel, very fast denoising algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fast averaging peer group filter for the impulsive noise removal in color images

Maliński

Smołka

2015

J Real-Time Image Proc

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In the paper, a new approach to the impulsive noise removal in color images is presented. The new filtering design is based on the peer group concept, which determines the membership of a central pixel of the filtering window to its local neighborhood, in terms of the number of close pixels. Two pixels are declared as close if their distance in a given color space does not exceed a predefined threshold value. A pixel is treated as not corrupted by the impulsive noise process, if its peer group consists of at least two close pixels, otherwise this pixel is replaced by a weighted average of uncorrupted samples from the local neighborhood. The peer group size assigned to each pixel is used for the averaging operation, so that pixels which have many peers are taken with higher weight. The new filtering design proved to restore efficiently color images corrupted by even strong impulsive noise, while preserving tiny image details. The beneficial property of the proposed filter is its very low computational complexity, which allows its application in real-time image processing tasks.

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“…In recent years, in a different discipline, much attention also has been paid increasingly to many other kinds of random noises. Such noises include multiplicative noise, see for instance [2,5,14,21]; impulse noise and Poisson noise etc., see for instance [4,6,7,15,26] and [9,20]. In this paper, we focus on the deblurring issues under the multiplicative noise.…”

Section: Introductionmentioning

confidence: 99%

The Convex Relaxation Method on Deconvolution Model withMultiplicative Noise

Huang

Zeng

2013

Commun. comput. phys.

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Abstract. In this paper, we consider variational approaches to handle the multiplicative noise removal and deblurring problem. Based on rather reasonable physical blurring-noisy assumptions, we derive a new variational model for this issue. After the study of the basic properties, we propose to approximate it by a convex relaxation model which is a balance between the previous non-convex model and a convex model. The relaxed model is solved by an alternating minimization approach. Numerical examples are presented to illustrate the effectiveness and efficiency of the proposed method.

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Fast Image Restoration Methods for Impulse and Gaussian Noises Removal

Cited by 64 publications

References 15 publications

PARIGI: a Patch-based Approach to Remove Impulse-Gaussian Noise from Images

PARIGI: a Patch-based Approach to Remove Impulse-Gaussian Noise from Images

Fast averaging peer group filter for the impulsive noise removal in color images

The Convex Relaxation Method on Deconvolution Model withMultiplicative Noise

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