An adaptive strategy for the restoration of textured images using fractional order regularization

R. H. CHAN; A. LANZA; S. MORIGI; F. SGALLARI

doi:10.4208/nmtma.2013.mssvm15

An adaptive strategy for the restoration of textured images using fractional order regularization

R. H. CHAN, A. LANZA, S. MORIGI, F. SGALLARI^*

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

76 Citations (Scopus)

Abstract

Total variation regularization has good performance in noise removal and edge preservation but lacks in texture restoration. Here we present a texture-preserving strategy to restore images contaminated by blur and noise. According to a texture detection strategy, we apply spatially adaptive fractional order diffusion. A fast algorithm based on the half-quadratic technique is used to minimize the resulting objective function. Numerical results show the effectiveness of our strategy.

Original language	English
Pages (from-to)	276-296
Number of pages	21
Journal	Numerical Mathematics
Volume	6
Issue number	1
Early online date	11 Jan 2013
DOIs	https://doi.org/10.4208/nmtma.2013.mssvm15
Publication status	Published - Feb 2013
Externally published	Yes

Funding

This work has been partially supported by MIUR-Prin 2008, ex60% project by University of Bologna Funds for selected research topics and by GNCS-INDAM.

Keywords

Deblurring
Fractional order derivatives
Ill-posed problem
Regularizing iterative method

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10.4208/nmtma.2013.mssvm15

Cite this

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abstract = "Total variation regularization has good performance in noise removal and edge preservation but lacks in texture restoration. Here we present a texture-preserving strategy to restore images contaminated by blur and noise. According to a texture detection strategy, we apply spatially adaptive fractional order diffusion. A fast algorithm based on the half-quadratic technique is used to minimize the resulting objective function. Numerical results show the effectiveness of our strategy.",

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AU - MORIGI, S.

AU - SGALLARI, F.

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Y1 - 2013/2

N2 - Total variation regularization has good performance in noise removal and edge preservation but lacks in texture restoration. Here we present a texture-preserving strategy to restore images contaminated by blur and noise. According to a texture detection strategy, we apply spatially adaptive fractional order diffusion. A fast algorithm based on the half-quadratic technique is used to minimize the resulting objective function. Numerical results show the effectiveness of our strategy.

AB - Total variation regularization has good performance in noise removal and edge preservation but lacks in texture restoration. Here we present a texture-preserving strategy to restore images contaminated by blur and noise. According to a texture detection strategy, we apply spatially adaptive fractional order diffusion. A fast algorithm based on the half-quadratic technique is used to minimize the resulting objective function. Numerical results show the effectiveness of our strategy.

KW - Deblurring

KW - Fractional order derivatives

KW - Ill-posed problem

KW - Regularizing iterative method

UR - https://www.scopus.com/pages/publications/84874065691

U2 - 10.4208/nmtma.2013.mssvm15

DO - 10.4208/nmtma.2013.mssvm15

M3 - Journal Article (refereed)

AN - SCOPUS:84874065691

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An adaptive strategy for the restoration of textured images using fractional order regularization

Abstract

Funding

Keywords

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