Improved decision based nonlinear Edge preserving algorithms for high Density noise removal in images

Abstract

Digital images are often corrupted by different types of noise namely newlineadditive white Gaussian noise impulse noise and mixed Gaussian and newlineimpulse noise Hence image denoising is one of the most common and newlineimportant image processing operations in image and video processing newlinesystems Many linear and nonlinear filtering algorithms have been proposed newlinein literature for image denoising Image edges and image details have high newlinefrequency content and carry very important information for visual perception newlineFilters having good edge and image detail preservation properties are highly newlinesuitable for digital image filtering Linear filters generally blur sharp edges newlinedestroy lines and other fine details present in the image This has led to a newlinesearch for nonlinear filtering alternatives In this thesis some improved newlinedecision based nonlinear edge preserving algorithms to remove high density newlinenoise in images are presented The proposed filters are simple and effective in newlinesuppressing different types of noise in the image with good edge and fine newlinedetail preserving characteristics newline newline