Multi-input-output Fusion Attention Module for Deblurring Networks

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PubDate: January 2022

Teams: Xiamen University of Technology

Writers: Yiqing Fan; Chaoqun Hong; Xiaodong Wang; Zhiqiang Zeng; Zetian Guo

PDF: Multi-input-output Fusion Attention Module for Deblurring Networks

Abstract

In recent years, coarse-to-fine networks have shown good performance in image deblurring. The image details recovered better by inputting multi-scale images, while the computation will be high. To design a efficient deblurring network, we use a new coarse-to-fine strategy for image deblurring. We use UNet as the backbone network, feeding one image of different scales at each layer of the network to obtain sharp images with better details. In addition, we have designed information supplement blocks that can effectively supplement the information of different scale images. Finally, to be able to obtain better information about the image, we introduce an attention mechanism. It is experimentally shown that our network performs well on different metrics.

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