Haze video image Clarity Processing Based on Optical Flow Threshold
Authors: 
Ru Chen, Xijuan WangAuthors Info & Claims
Ru Chen, Xijuan WangAuthors Info & ClaimsAbstract
In view of the problem of haze weather on the visual effect of video image, which causes the picture distortion, image quality degradation and definition blur of video image, a defogging processing method of haze video image based on optical flow threshold is proposed so as to restore the real and natural color image. Firstly, extract the image of the t frame at time t, track the characteristics of the image at time t + 1 to time t + n, extract the image of the t+n frame, then calculate the optical flow values of the t frame and the t + n frame, make a difference between the obtained optical flow values to obtain the optical flow threshold, compare the obtained optical flow threshold with the given threshold, if the value is greater than or equal to the given threshold, take the optical flow threshold intermediate frame image, and the middle frame and t+n frame images are processed by Retinex algorithm, and this operation is performed iteratively. Finally, the processed single frame video sequence is merged into a whole and output. The experiment shows that the processing speed of the algorithm is 0.07, much lower than other processing methods, which verifies the effectiveness and innovativeness of the proposed algorithm.
References
[1]
Wang Keping,Yang Yi,Fei Shumin. A review of haze image clarification algorithms [J/OL]. Journal of Intelligent Systems:1-16.
[2]
Shi Y, Xiang XG. A haze video compression method based on motion estimation sharing[J]. Computer and Digital Engineering, 2021, 49(03):550-555.
[3]
L and E H. Recent advances in the Retinex theory and some implications for cortical computations: Color vision and the natural image[J]. Proceedings of the National Academy Sciences of the United States of America, 1983, 80(16): 5163- 5169.
[4]
LI Wang, YANG Jinbao, SUN Ting, FU Lingling. Retinex-based multi-scale single-image defogging network[J]. Journal of Qingdao University (Natural Science Edition),2022,35(04):26-32.
[5]
Liu Weihua, Xue Yansong, Yichen, Wang Fuping. A low-light image enhancement algorithm combining multi-scale deep learning network and Retinex theory[J/OL]. Signal Processing:1-12.
[6]
Li, Can-Lin, Zhu, Jin-Juan, Liu, Jin-Hua, Bi, Li-Hua. An adaptive SSR method for low illumination image enhancement in foggy days[J]. Computer Application and Software,2022,39(09):233-239+268.
[7]
Shao X. Q., Yang Y., Liu Y. L. A review of research on optical flow algorithms for fluid motion estimation[J]. Chinese Journal of Graphics, 2021, 26(02):355-367.
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- Haze video image Clarity Processing Based on Optical Flow Threshold
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Published In
March 2023
598 pages
ISBN:9781450399449
DOI:10.1145/3590003
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 May 2023
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CACML 2023
CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
March 17 - 19, 2023
Shanghai, China
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CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
Overall Acceptance Rate 93 of 241 submissions, 39%
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