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Video smoke removal based on low‐rank tensor completion via spatial‐temporal continuity constraint
Author(s) -
Zhu Hu,
Xu Guoxia,
Liu Lu,
Deng Lizhen
Publication year - 2021
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6169
Subject(s) - smoke , constraint (computer aided design) , computer science , rank (graph theory) , consistency (knowledge bases) , tensor (intrinsic definition) , local consistency , computer vision , mathematical optimization , artificial intelligence , algorithm , mathematics , constraint satisfaction problem , engineering , geometry , combinatorics , probabilistic logic , pure mathematics , waste management
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.