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|a 10.1109/TPAMI.2022.3226276
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|a eng
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|a Liu, Jun
|e verfasserin
|4 aut
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|a Rank-One Prior
|b Real-Time Scene Recovery
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|c 2023
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|a Text
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|a Date Completed 06.06.2023
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|a Date Revised 06.06.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Scene recovery is a fundamental imaging task with several practical applications, including video surveillance and autonomous vehicles, etc. In this article, we provide a new real-time scene recovery framework to restore degraded images under different weather/imaging conditions, such as underwater, sand dust and haze. A degraded image can actually be seen as a superimposition of a clear image with the same color imaging environment (underwater, sand or haze, etc.). Mathematically, we can introduce a rank-one matrix to characterize this phenomenon, i.e., rank-one prior (ROP). Using the prior, a direct method with the complexity O(N) is derived for real-time recovery. For general cases, we develop ROP + to further improve the recovery performance. Comprehensive experiments of the scene recovery illustrate that our method outperforms competitively several state-of-the-art imaging methods in terms of efficiency and robustness
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|a Journal Article
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|a Liu, Ryan Wen
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|a Sun, Jianing
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|a Zeng, Tieyong
|e verfasserin
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 7 vom: 02. Juli, Seite 8845-8860
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|u http://dx.doi.org/10.1109/TPAMI.2022.3226276
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