A HVS-directed neural-network-based approach for impulse-noise removal from highly corrupted images

In this paper, a novel two-stage noise removal algorithm to deal with fixed-value impulse noise is proposed. In the first stage, the decision-based recursive adaptive median filter is applied to remove the noise cleanly and keep the uncorrupted information as well as possible. In the second stage, the fuzzy decision rules inspired by human visual system (HVS) are proposed to classify pixels of the image into human perception sensitive class and non-sensitive class. A neural network is proposed to enhance the sensitive regions to perform better visual quality. According to the experiment results, the proposed method is superior to conventional methods in perceptual image quality as well as the clarity and the smoothness in edge regions.