Denoising convolutional neural network with mask for salt and pepper noise

Abstract

In this study, the authors propose a new loss function for denoising convolutional neural network (DnCNN) for salt- and-pepper noise (SPN). Based on the motivation of utilising the mask of SPN, firstly from the usual SPN-denoising restoration equation, the authors establish a perfect restoration condition; the restored image is precisely the clean image if this condition holds. Then they design a mask-involved loss function to encourage the network to satisfy this condition in training progress. Experimental results demonstrate that compared with general DnCNN and other state-of-the-art SPN denoising methods, DnCNN equipped with the proposed loss function involving mask (MaskDnCNN) is more effective, robust and efficient.