Rank-One Prior: Real-Time Scene Recovery

Jun Liu, Ryan Wen Liu, Jianing Sun, Tieyong Zeng

Abstract: 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 <inline-formula><tex-math notation="LaTeX">$O(N)$</tex-math></inline-formula> is derived for real-time recovery. For general cases, we develop ROP <inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> 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.