基于广度残差与像素点注意力的图像去模糊模型

doi:10.3969/j.issn.0255-8297.2022.06.010

Abstract

Abstract: In order to solve the problem that existing methods suffer difficulty in quickly recovering high-quality sharp images from blurred images, an image deblurring model based on width residual and pixel attention is proposed. Based on encoder-decoder networks, the model uses wide convolution and multi-order residual method to construct width residual modules, improving the processing speed of the model. At the same time, local average and matrix cross multiplication are used to construct pixel attention modules, which enhance the model deblurring quality. The experimental results on GOPRO datasets show that the structural similarity of the proposed method is 0.922 3, the peak signal-to-noise ratio is 31.74 dB, and the average running time is 0.37 seconds when the model size is 22.24 MB. Compared with the scale-recurrent network method, the peak signal-to-noise ratio of the proposed method improves by 4%, and its performance is better than the other existing deblurring methods.

Key words: image deblurring, encoder-decoder network, pixel attention, width residual

CLC Number:

TP391.4

KUANG Fa, XIONG Bangshu, OU Qiaofeng, YU Lei. Image Deblurring Model Based on Width Residual and Pixel Attention[J]. Journal of Applied Sciences, 2022, 40(6): 996-1005.

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Image Deblurring Model Based on Width Residual and Pixel Attention

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