Open AccessA Novel Approach for Image Denoising and Performance Analysis using SGO and APSO
Author(s) -
Vikas Gupta,
K. V. S. Murthy,
Ravi Shankar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2070/1/012139
Subject(s) - particle swarm optimization , thresholding , peak signal to noise ratio , noise reduction , wavelet , artificial intelligence , mean squared error , image (mathematics) , matlab , computer science , pattern recognition (psychology) , noise (video) , non local means , image quality , mathematics , image processing , algorithm , statistics , operating system
Image denoising is essential to extract the information contained in an image without errors. A technique of using both wavelets and evolutionary computing tools is proposed to denoise and to improve the image quality. An adaptive thresholding-based wavelet denoising technique in the threshold function is coordinated by novel social group optimization (SGO) and accelerated particle swarm optimization (APSO) is proposed. The simulation oriented experimentation is taken out employing MATLAB and the analysis is carried out using the image property metrics similar to peak signal to noise ratio (PSNR), mean square error (MSE) and other structural similarity index metrics (SSIM).