Improved Adaptive Impulsive Noise Suppression

Abstract

In this work an improved scheme for eliminating impulsive noise of varying strengths from corrupted images is proposed. A neural network is employed to classify the corrupted and non-corrupted pixels. Filtering is only carried out on corrupted pixels keeping the non-corrupted ones intact. Emphasis has been put on selection of relevant input and training patterns. With appropriate choice of patterns the assiduous task of training has become effortless as well as the noise detection become reliable. Comparative analysis with competent schemes on standard images at different noise conditions shows that the proposed scheme outperforms its counterparts.