Learning a Deep Convolutional Network for Subband Image Denoising

Jing Zhao, Ruiqin Xiong, Jizheng Xu, Feng Wu, Tiejun Huang

Abstract: Due to the fast inference and excellent learning capability, deep learning has become an effective means for image denoising and attracted considerable attention recently. However, for the images with rich textures and structures, the performance of deep learning approaches is still unsatisfactory. To address this issue, we develop a new convolutional neural network (CNN) for subband image denoising and name it SDCNN. In the proposed approach, we first decompose images into transform domain and denoise the coefficients of various subbands. By incorporating frequency information with spatial context, SDCNN is more effective in recovering image details. In particular, the introduced procedure of subband transform also plays the role of downsampling and enlarges the receptive field without increasing depth or sacrificing efficiency of network. Experimental results show that the SDCNN achieves promising results in terms of both objective and subjective performance.