Variational approach for restoring blurred images with cauchy noise

Federica Sciacchitano; Yiqiu Dong; Tieyong Zeng

doi:10.1137/140997816

Variational approach for restoring blurred images with cauchy noise

Federica Sciacchitano, Yiqiu Dong, Tieyong Zeng

Hong Kong Baptist University

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Abstract

The restoration of images degraded by blurring and noise is one of the most important tasks in image processing. In this paper, based on the total variation (TV) we propose a new variational method for recovering images degraded by Cauchy noise and blurring. In order to obtain a strictly convex model, we add a quadratic penalty term, which guarantees the uniqueness of the solution. Due to the convexity of our model, the primal dual algorithm is employed to solve the minimization problem. Experimental results show the effectiveness of the proposed method for simultaneously deblurring and denoising images corrupted by Cauchy noise. Comparison with other existing and well-known methods is provided as well.

Original language	English
Journal	S I A M Journal on Imaging Sciences
Volume	8
Issue number	3
Pages (from-to)	1894-1922
ISSN	1936-4954
DOIs	https://doi.org/10.1137/140997816
Publication status	Published - 2015

Keywords

Cauchy noise
Image deblurring
Image denoising
Primal dual algorithm
Total variation regularization
Variational model

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SIIMS 099781 ggAccepted author manuscript, 4.37 MB

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title = "Variational approach for restoring blurred images with cauchy noise",

abstract = "The restoration of images degraded by blurring and noise is one of the most important tasks in image processing. In this paper, based on the total variation (TV) we propose a new variational method for recovering images degraded by Cauchy noise and blurring. In order to obtain a strictly convex model, we add a quadratic penalty term, which guarantees the uniqueness of the solution. Due to the convexity of our model, the primal dual algorithm is employed to solve the minimization problem. Experimental results show the effectiveness of the proposed method for simultaneously deblurring and denoising images corrupted by Cauchy noise. Comparison with other existing and well-known methods is provided as well.",

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AU - Sciacchitano, Federica

AU - Dong, Yiqiu

AU - Zeng, Tieyong

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N2 - The restoration of images degraded by blurring and noise is one of the most important tasks in image processing. In this paper, based on the total variation (TV) we propose a new variational method for recovering images degraded by Cauchy noise and blurring. In order to obtain a strictly convex model, we add a quadratic penalty term, which guarantees the uniqueness of the solution. Due to the convexity of our model, the primal dual algorithm is employed to solve the minimization problem. Experimental results show the effectiveness of the proposed method for simultaneously deblurring and denoising images corrupted by Cauchy noise. Comparison with other existing and well-known methods is provided as well.

AB - The restoration of images degraded by blurring and noise is one of the most important tasks in image processing. In this paper, based on the total variation (TV) we propose a new variational method for recovering images degraded by Cauchy noise and blurring. In order to obtain a strictly convex model, we add a quadratic penalty term, which guarantees the uniqueness of the solution. Due to the convexity of our model, the primal dual algorithm is employed to solve the minimization problem. Experimental results show the effectiveness of the proposed method for simultaneously deblurring and denoising images corrupted by Cauchy noise. Comparison with other existing and well-known methods is provided as well.

KW - Cauchy noise

KW - Image deblurring

KW - Image denoising

KW - Primal dual algorithm

KW - Total variation regularization

KW - Variational model

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DO - 10.1137/140997816

M3 - Journal article

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EP - 1922

JO - S I A M Journal on Imaging Sciences

JF - S I A M Journal on Imaging Sciences

IS - 3

ER -

Variational approach for restoring blurred images with cauchy noise

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