Video smoke removal based on low‐rank tensor completion via spatial‐temporal continuity constraint. (26th January 2021)

• Record Type: Journal Article
• Authors: Zhu, Hu; Xu, Guoxia; Liu, Lu; Deng, Lizhen
• Is Part Of: Concurrency and computation. Volume 33:Number 15(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-26
• Subjects: smoke removal -- spatial continuity -- tensor completion; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6169
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Smoke has a very bad effect on the outdoor vision system. Not only are the videos with poor visual effects obtained, but also the quality and structure of the videos are reduced. In this paper, we propose a video smoke removal method based on low‐rank tensor completion via spatial‐temporal continuity constraint. The proposed method is based on the smoke mixing model and consider the sparseness of smoke and the global and local consistency of clean video. Then, the optimal solution of the smoke removal algorithm model is quickly realized by the Alternating Direction Method of Multiplier. Finally, we evaluate the experiment results of real‐world data and simulated data from the visual effects and objective indicators. And the experiment results show that our proposed algorithm can achieve better smoke removal results.