  In this paper, 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
paper are as follows: First, by applying concise but effective concatenation
and feature selection mechanism, we establish a novel connection mechanism
which connect different modules in the modules stacking network. Second, both
pixel-wise and channel-wise attention mechanisms are used in each module
convolution layer, which promotes further extraction of more essential
information 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.
