Video deblurring network based on dark light enhancement

Yangyang Zhong; Zunjie Zhu; Zhefeng Xu

doi:10.1117/12.2656787

20 October 2022 Video deblurring network based on dark light enhancement

Yangyang Zhong, Zunjie Zhu, Zhefeng Xu

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124511Z (2022) https://doi.org/10.1117/12.2656787
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

At present, the deblurring algorithms based on deep learning have achieved good performance, but the current deblurring public data sets are obtained in the scene with good light conditions In the actual scene, when the photosensitive device collects images, it is often disturbed by the external environment, such as night, overcast and other environments, which will cause the quality of the collected images to become worse, and will bring greater challenges to the deblurring task This paper proposes an end-to-end depth network for video deblurring based on dark light enhancement Firstly, this paper proposes an end-to-end video deblurring network based on STFAN; Secondly, the dark light enhancement module is proposed, which can improve the dark light image quality and further enhance the deblurring effect; Finally, we introduce our own dark light scene data set, and train and verify the proposed method on the dark light data set Experimental results show that the proposed algorithm has better deblurring effect in dark scenes than other deblurring algorithms High performance dark light deblurring algorithm will play a vital role in the fields of photography, photography, medicine and criminal investigation.

Citation Download Citation

Yangyang Zhong, Zunjie Zhu, and Zhefeng Xu "Video deblurring network based on dark light enhancement", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124511Z (20 October 2022); https://doi.org/10.1117/12.2656787

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KEYWORDS

Video

Convolution

Image enhancement

Fiber optic gyroscopes

RGB color model

Fuzzy logic

Feature extraction

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