arrow
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

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

Published online: 2013-06

Export citation
  • 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.

  • AMS Subject Headings

65F10, 65F22, 65K10

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{NMTMA-6-276, author = {}, 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 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 & F. Sgallari. (2020). 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
Copy to clipboard
The citation has been copied to your clipboard