A Global Variational Filter for Restoring Noised Images with Gamma Multiplicative Noise
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.
Keywords:
multiplicative gamma noise, restoration, regularization, data fitting, total variation, MAP estimation, proximal operator, PSNR, SSIM, VSNRDownloads
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