Impulse Noise Removal from Gray Scale Images Based on ANN Classification Based Fuzzy Filter

Abstract

In this paper, artificial neural network (ANN) based fuzzy filter is proposed for removal of impulse noise from gray images. ANN is used for classification of noisy and non-noisy pixels from the image corrupted by impulse noise. Based on the classification, fuzzy filtering is done adjusting the corrupted and non-corrupted pixels. In this method, feature set comprises of predicted error, absolute difference between the median and processing kernel, pixel under operation and median value within the kernel. It has been observed that this proposed method increases peak-signal-to-noise ratio (PSNR) not only for low density of noise but also for high density of noise. This method maintains structural similarity of the original image from that corrupted one to a great extent. It reduces computation time of the removal process while removing noise from the corrupted image. It is shown in this work how this proposed method outperforms other conventional filters.