Open AccessRemoval of Multiplicative Gamma Noise from Images via SRAD Model Amelioration
Author(s) -
Nacira Diffellah,
Rabah Hamdini,
Tewfik Bekkouche
Publication year - 2021
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.4527
Subject(s) - anisotropic diffusion , image (mathematics) , multiplicative function , homogeneous , artificial intelligence , computer vision , mathematics , computer science , value (mathematics) , noise (video) , statistics , mathematical analysis , combinatorics
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.