Enhanced Dual Adversarial Network for Real Image Noise Removal and Generation using Edge Loss Function

Many methods have been proposed to address the real noise, they suffer from restoring the edge regions appropriately. Because most convolutional neural network-based denoising methods capture noise characteristics through pixel loss that only detects contaminated pixels, high frequency components cannot be considered. This causes blurs and artifacts on edge regions which has the high frequency component. In this paper, we apply an edge loss function to the dual adversarial network to deal with this issue. Using the edge loss and the pixel loss together, the network has been improved to restore not only the actual intensity but also the edges effectively.