Simultaneously Learning Deep Quaternion Reconstruction and Noise Convolutional Dictionary for Color Image Denoising

Abstract

Recently, many deep convolutional dictionary learning-based methods, integrating the traditional image representation methods with deep neural networks, have achieved great success in various image processing tasks. However, the existing approaches can be further improved with the following considerations: (1) They congenitally suffer from the high cross-channel correlation loss for color image processing tasks since they usually treat each color channel independently, not in a whole perspective. (2) They only build up a single reconstruction dictionary learning model to directly approximate images using several single dictionary atoms, which cannot make full use of the representative ability of the model. In this paper, we propose a simultaneously learning deep quaternion reconstruction and noise convolutional dictionary model. To fully explore the cross-channel correlation, we use the quaternion method to process the color image in a holistic way. An adaptive attentional weight of reconstruction and noise learning module is also developed for the optimal combination between reconstruction and noise learning. Experimental results for synthesis and real color image denoising have demonstrated the superiority of the proposed method over other state-of-the-art methods.