Image Denoising Using Deep CGAN With Bi-Skip Connections
Peng Wang · 2019
With the rapid development of neural networks, many deep learning-based image processing tasks have shown outstanding performance. In this paper, we describe a unified deep learning-based approach for image image denoising. The proposed method is composed of deep convolutional neural and conditional generative adversarial networks. For the discriminator network, we present a new network architecture with bi-skip connections to address hard training and details losing issues. In the generative network, a objective optimization is derived to solve the problem of common conditions being non-identical. Through extensive experiments on image denoising task on both qualitative and quantitative criteria, we demonstrate that our proposed method performs favorably against current state-of-the-art approaches.