An L0-regularized global anisotropic gradient prior for single-image de-raining

Abstract We propose a method based on a global anisotropic gradient prior (GAGP) for addressing the problem of rain streak removal. We observe that both images and rain streaks have anisotropy at edges, and that the rain distribution in images is sparse. We therefore propose an L0-regularized sparse term with an L0-regularized GAGP term to efficiently detect the rain streaks. We applied the L0-regularized GAGP as the image regularization term to protect the image structure and details. We also developed an alternating half-quadratic algorithm to solve the proposed L0 optimization model by introducing the variable splitting method and analyzing the convergence of the algorithm. Experimental results show that the proposed method outperforms state-of-the-art methods in rain streak removal and preserving image structure on both synthesized and real-world images with different levels and types of rain.