Improved blind motion deblurring method

Xianfeng He; Shan Tian; Jiandan Zhong; Chao Chen

doi:10.1117/12.2604576

5 October 2021 Improved blind motion deblurring method

Xianfeng He, Shan Tian, Jiandan Zhong, Chao Chen

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119111E (2021) https://doi.org/10.1117/12.2604576
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

As one of several common blurs, motion blur images are quite common in life. There are many ways to remove motion blur, but each has some problems, such as checkerboard artifacts, poor restoration of image texture details, and high algorithm costs. This paper proposes an improved blind motion deblurring method with Generative Adversarial Network, which achieved good results. The method in this paper uses a combination of up-sampling and convolution to replace the deconvolution of the traditional generative adversarial network model, effectively removing the common checkerboard artifacts in image processing, and the problems of poor texture detail restoration. The results show that the method in the article has achieved the expected purpose, it has achieved obvious deblurring effects from both objective and subjective evaluation indicators.

Citation Download Citation

Xianfeng He, Shan Tian, Jiandan Zhong, and Chao Chen "Improved blind motion deblurring method", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119111E (5 October 2021); https://doi.org/10.1117/12.2604576

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KEYWORDS

Gallium nitride

Convolution

Cameras

Deconvolution

Evolutionary algorithms

Data modeling

Image processing

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