A novel detection and removal scheme for denoising images corrupted with Gaussian outliers

Proper choice of denoising filter is a very important requirement for efficient image restoration because most of the filters only reduce the effect of the noise rather than removing it. In this paper, a novel algorithm for filtering of gaussian outliers based on the local features of the image is proposed. The algorithm first categorizes the pixels into edge, texture and noise points and then restores the corrupted image using the adaptive neighborhood concept. The proposed algorithm is objectively evaluated by using the PSNR and MAE parameters. Simulation results indicate a marked improvement in restoration quality in comparison to other methods.