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Volume 3, Issue 4
New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise

Yiqiu Dong & Tieyong Zeng

DOI: 10.4208/eajam.240713.120813a

East Asian J. Appl. Math., 3 (2013), pp. 263-282.

Published online: 2018-02

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  • Abstract

A new hybrid variational model for recovering blurred images in the presence of multiplicative noise is proposed. Inspired by previous work on multiplicative noise removal, an I-divergence technique is used to build a strictly convex model under a condition that ensures the uniqueness of the solution and the stability of the algorithm. A split-Bregman algorithm is adopted to solve the constrained minimisation problem in the new hybrid model efficiently. Numerical tests for simultaneous deblurring and denoising of the images subject to multiplicative noise are then reported. Comparison with other methods clearly demonstrates the good performance of our new approach.

  • Keywords

Convex model, image deblurring, multiplicative noise, Split-Bregman Algorithm, total variation, variational model.

  • AMS Subject Headings

52A41, 65K05, 65K15, 90C25, 90C30

  • Copyright

COPYRIGHT: © Global Science Press

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  • TXT
@Article{EAJAM-3-263, author = {Yiqiu Dong and Tieyong Zeng}, title = {New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise}, journal = {East Asian Journal on Applied Mathematics}, year = {2018}, volume = {3}, number = {4}, pages = {263--282}, abstract = {

A new hybrid variational model for recovering blurred images in the presence of multiplicative noise is proposed. Inspired by previous work on multiplicative noise removal, an I-divergence technique is used to build a strictly convex model under a condition that ensures the uniqueness of the solution and the stability of the algorithm. A split-Bregman algorithm is adopted to solve the constrained minimisation problem in the new hybrid model efficiently. Numerical tests for simultaneous deblurring and denoising of the images subject to multiplicative noise are then reported. Comparison with other methods clearly demonstrates the good performance of our new approach.

}, issn = {2079-7370}, doi = {https://doi.org/10.4208/eajam.240713.120813a}, url = {http://global-sci.org/intro/article_detail/eajam/10920.html} }
TY - JOUR T1 - New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise AU - Yiqiu Dong & Tieyong Zeng JO - East Asian Journal on Applied Mathematics VL - 4 SP - 263 EP - 282 PY - 2018 DA - 2018/02 SN - 3 DO - http://doi.org/10.4208/eajam.240713.120813a UR - https://global-sci.org/intro/article_detail/eajam/10920.html KW - Convex model, image deblurring, multiplicative noise, Split-Bregman Algorithm, total variation, variational model. AB -

A new hybrid variational model for recovering blurred images in the presence of multiplicative noise is proposed. Inspired by previous work on multiplicative noise removal, an I-divergence technique is used to build a strictly convex model under a condition that ensures the uniqueness of the solution and the stability of the algorithm. A split-Bregman algorithm is adopted to solve the constrained minimisation problem in the new hybrid model efficiently. Numerical tests for simultaneous deblurring and denoising of the images subject to multiplicative noise are then reported. Comparison with other methods clearly demonstrates the good performance of our new approach.

Yiqiu Dong and Tieyong Zeng. (2018). New Hybrid Variational Recovery Model for Blurred Images with Multiplicative Noise. East Asian Journal on Applied Mathematics. 3 (4). 263-282. doi:10.4208/eajam.240713.120813a
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