CANet: Concatenated Attention Neural Network for Image Restoration

Yingjie Tian, Yiqi Wang, LinRui Yang, Zhiquan Qi

Abstract: In this article, we present a general framework for low-level vision tasks including image compression artifacts reduction and image denoising. Under this framework, a novel concatenated attention neural network (CANet) is specifically designed for image restoration. The main contributions of this article are as follows: First, we establish a concise network with a recursive topology by abandoning most complex artificially designed topology of the network. Second, we did not use the down-sampling mechanism in our network and a lightweight attention mechanism is adopted for this. Third, we rethink the feature fusion mechanism in the field of image restoration and improve this by applying concise but effective concatenation and feature selection mechanism which promotes further extraction of more cleaner features in images. Lastly, we demonstrate that CANet achieves better results than previous state-of-the-art approaches with sufficient experiments in compression artifacts removing and image denoising.