Concatenated Attention Neural Network for Image Restoration

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 pap...

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Bibliographic Details
Published in:arXiv.org 2020-06
Main Authors: Tian YingJie, Wang, YiQi, Yang LinRui, Qi ZhiQuan
Format: Article
Language:English
Subjects:
Convolution
Image compression
Image restoration
Modules
Neural networks
Noise reduction
Online Access:Get full text
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Summary: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.
ISSN:2331-8422