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Volume 6, Issue 1
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

Numer. Math. Theor. Meth. Appl., 6 (2013), pp. 276-296.

Published online: 2013-06

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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.

  • Keywords

Ill-posed problem, deblurring, fractional order derivatives, regularizing iterative method.

  • AMS Subject Headings

65F10, 65F22, 65K10

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{NMTMA-6-276, author = {R. H. Chan, A. Lanza, S. Morigi and F. Sgallari}, title = {An Adaptive Strategy for the Restoration of Textured Images Using Fractional Order Regularization}, journal = {Numerical Mathematics: Theory, Methods and Applications}, year = {2013}, volume = {6}, number = {1}, pages = {276--296}, 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.

}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.2013.mssvm15}, url = {http://global-sci.org/intro/article_detail/nmtma/5904.html} }
TY - JOUR T1 - An Adaptive Strategy for the Restoration of Textured Images Using Fractional Order Regularization AU - R. H. Chan, A. Lanza, S. Morigi & F. Sgallari JO - Numerical Mathematics: Theory, Methods and Applications VL - 1 SP - 276 EP - 296 PY - 2013 DA - 2013/06 SN - 6 DO - http://doi.org/10.4208/nmtma.2013.mssvm15 UR - https://global-sci.org/intro/article_detail/nmtma/5904.html KW - Ill-posed problem, deblurring, fractional order derivatives, regularizing iterative method. 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.

R. H. Chan, A. Lanza, S. Morigi and F. Sgallari. (2013). An Adaptive Strategy for the Restoration of Textured Images Using Fractional Order Regularization. Numerical Mathematics: Theory, Methods and Applications. 6 (1). 276-296. doi:10.4208/nmtma.2013.mssvm15
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