Denoising convolutional neural network with mask for salt and pepper noise
Jiuning Chen, Fang Li · IET Image Processing · 2019
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.