Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 45, Issue 7, Pages 8845-8860Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2022.3226276
Keywords
Haze; rank-one prior; sand dust; underwater; unified spectrum
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In this article, a new real-time scene recovery framework is proposed to restore degraded images under different weather/imaging conditions. The method introduces a rank-one matrix to characterize the degradation phenomenon and achieves real-time recovery. Experimental results demonstrate that the proposed method outperforms several state-of-the-art imaging methods in terms of efficiency and robustness.
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 OoNTHORN is derived for real-time recovery. For general cases, we develop ROPthorn 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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