A Novel Approach for Video De-Noising using Convex Optimization
Alaa Abdel-Hakim (Assiut University, Egypt)
We propose a novel approach for rain/snow removal in videos using low-rank recovery. Rain/snow-distorted 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 formulate the problem in a convex optimization form. Then, EALM method is used to solve the model for the low-rank term, which represents the rain/snow-free signal. 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 dynamic videos. The evaluation results proved the effectiveness of the proposed approach when compared to the existing approaches.
Journal: International Journal of Simulation- Systems, Science and Technology- IJSSST V15
Published: Jun 30, 2014