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Concatenated Attention Neural Network for Image Restoration

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Document pages: 15 pages

Abstract: In this paper, we present a general framework for low-level vision tasksincluding image compression artifacts reduction and image denoising. Under thisframework, a novel concatenated attention neural network (CANet) isspecifically designed for image restoration. The main contributions of thispaper are as follows: First, by applying concise but effective concatenationand feature selection mechanism, we establish a novel connection mechanismwhich connect different modules in the modules stacking network. Second, bothpixel-wise and channel-wise attention mechanisms are used in each moduleconvolution layer, which promotes further extraction of more essentialinformation in images. Lastly, we demonstrate that CANet achieves betterresults than previous state-of-the-art approaches with sufficient experimentsin compression artifacts removing and image denoising.

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