A Novel Approach for Rain Removal from Videos Using Low-Rank Recovery

Abstract

We propose a novel approach for rain/snow removal from videos using low-rank recovery. Rain/snowdistorted video frames are treated as a distorted 3D signal. The main goal is to separate the distortion, which is the rain or snow additive signal, from the original rain-free signal. Inter-frame information is exploited to put the problem in a convex optimization form. Then, the exact augmented Lagrangian multipliers (EALM) method is used to solve the model for the low-rank terms, which represent the rain/snow-free frames. The proposed approach has several advantages over the existing approaches. It is model-independent, i.e. it does not require shape, appearance, or speed models. Also, it does not need prior information about the acquisition environment. Three different sets of data were used for evaluation: synthetic data for simulation experiments to provide quantitative results, real static videos, and real dynamic videos. The evaluation results proved the effectiveness of the proposed approach when compared to the existing approaches.