Removal of Multiplicative Gamma Noise from Images via SRAD Model Amelioration

N Diffellah,R Hamdini,T Bekkouche

doi:10.48084/etasr.4527

Abstract

In this paper, an improved Speckle Reducing Anisotropic Diffusion (SRAD), destined to remove multiplicative gamma noise applied to different images is proposed. The basic idea is to divide the image into several riddled areas and then calculate the Equivalent Number of Look (ENL) of each region. The largest value of the ENL is the best optimal homogeneous region of the image. This optimal choice allows us to solve the major problem of the SRAD algorithm articulated around a visual choice of the homogeneous region which is not satisfactory and causes non-uniformity in this area. To give more validity to the proposed method, several experimentations were conducted using different kinds of images and were approved by some quantitative metrics like PSNR, SNR, VSNR, and SSIM. The computer simulation results confirm the efficiency of the proposed method which outperformances the classical SRAD method.

Highlights

Images are not immune to contamination by noise that can degrade their visual quality
Image 2: "First NAC Image Obtained in Mercury Orbit" [16] with a size of 1024×1020 by NASA Goddard Photo and Video, which is licensed under CC BY 2.0
An efficient approach for image denoising when the image is corrupted by Gamma multiplicative noise was presented

Summary

Introduction

Images are not immune to contamination by noise that can degrade their visual quality To deal with this problem, image denoising is the recommended solution. In this context, several restoration filtering algorithms have emerged. Authors in [1] proposed an algorithm called SRAD, which consists in exploiting the instantaneous coefficient of variation which is a function of local gradient magnitude, and Laplacian operators. This method gave good performance in terms of the quality of the restored images, but has the disadvantage of the choice of the homogeneous area

Methods

Results

Conclusion