A study on impulse noise removal for varied noise densities

Abstract

Digital images are generally affected by noise due to acquisition process or by transmission process. Most of the images are assumed to have variety of noise. Different algorithms are used depending on the noise model. Number of algorithms have been developed in the past years to denoise the images. In this paper different existing denoising algorithms for impulse noise are studied and compared. Almost all algorithms studied here are executed in two steps, first step is to detect the corrupted pixels and second step is to correct the pixels by replacing the filter estimated values. The performances of the algorithms are compared based on PSNR values. Some algorithms like median filter work better at low density level and the algorithms like RWMF are good at high density levels with changing window size.

Comparing the performance of various filters on stoned kidney images

“…It is nearly arisen in different imaging systems like laser, acoustics and SAR (Synthetic Aperture Radar) imagery. Speckle noise enriches the mean grey level of a local area [12,2]. This noise follows a gamma distribution as:…”

“…PSNR is the ratio between maximum possible power of a pixel value and the power of distorting noise which affects the quality of the original image [12]. It is defined by: …”