Edge-preserving rain removal for light field images based on RPCA
Cheen‐Hau Tan, Jie Chen, Lap‐Pui Chau · 2017
Rain deteriorates outdoor vision and causes challenge for most vision based intelligent systems. In this paper we propose a method to efficiently remove the rain present in light field data. Firstly, the sub-view image sequence is globally aligned to the central view. Robust Principle Component Analysis (RPCA) are then applied to decompose the sequence into two parts, i.e., the low-rank data, and the sparse data. The decomposed sparse data contains both rain streaks and scene disparity edges. We propose to compute a dark view image to estimate the non-rain disparity edges, and the remaining part of the decomposed sparse data will be considered as rain. The disparity edges will then be added back to the low-rank data. The proposed method produces satisfactory rain removal visual results, and can efficiently preserve the light field perspective disparity at the same time.