A Global Variational Filter for Restoring Noised Images with Gamma Multiplicative Noise

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 08 Jun 2019 France Publisher:Engineering, Technology & Applied Science ResearchJournal:Engineering, Technology & Applied Science Research, volume 9, pages 4,188-4,195 (issn: 2241-4487, eissn: 1792-8036,

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Authors: Diffellah, Nacira; Baarir, Zine-Eddine; Derraz, Foued; Taleb-Ahmed, Abdelmalik;

doi: 10.48084/etasr.2737 , 10.60692/9aksz-rsk24 , 10.5281/zenodo.3249109 , 10.5281/zenodo.3249108 , 10.60692/f6kr8-4a348

A Global Variational Filter for Restoring Noised Images with Gamma Multiplicative Noise

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Abstract

In this paper, we focus on a globally variational method to restore noisy images corrupted by multiplicative gamma noise. Our problem is assumed as a regularization problem in total variation (TV) framework with data fitting term which is deduced by maximizing the a-posteriori probability density (MAP estimation). We need to evaluate the proximal operator of a data fitting term then we numerically adapt the Douglas-Rachford (DR) splitting method to solve the problem. Our experiments use real images with different levels of noise. To validate the effectiveness of the proposed method, we compare the proposed method with other variational models. Our method shows effective suppression of noise, excellent edge preservation, and the measures of image quality such as PSNR (peak signal-to-noise ratio), VSNR (visual signal-to-noise ratio) and SSIM (structural similarity index) explain the proposed model΄s good performance.

Country

France

Related Organizations

University of Mohamed El Bachir El Ibrahimi
Algeria
University of Lille
France
University of Biskra
Algeria
University of Abou Bekr Belkaïd
Algeria
Polytechnic University of Hauts-de-France
France

Keywords

Artificial intelligence, Multispectral, Multiplicative noise, Filter (signal processing), Image restoration, Engineering, Regularization (linguistics), Noise reduction, Statistics, data fitting, proximal operator, Hyperspectral Imaging, Maximum a posteriori estimation, Gaussian Noise, regularization, VSNR, Algorithm, Analog signal, Peak signal-to-noise ratio, Physical Sciences, Computer Vision and Pattern Recognition, Signal transfer function, multiplicative gamma noise, restoration, [SPI] Engineering Sciences [physics], Single Image Super-Resolution Techniques, Noise measurement, Multiplicative function, Noise (video), Mathematical analysis, Multispectral and Hyperspectral Image Fusion, Image processing, Media Technology, FOS: Mathematics, Image (mathematics), Image quality, MAP estimation, Image Denoising, PSNR, Computer hardware, Digital signal processing, Computer science, total variation, SSIM, Computer Science, Computer vision, Image Denoising Techniques and Algorithms, Sparse Representations, Mathematics, Maximum likelihood

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