Motion robust rain detection and removal from videos

Weather such as rain and snow cause difficulties in processing the videos captured. Since the appearance of rain drops can affect the performance of human tracking and reduce the efficiency of video compression, detection and removal of rain is a challenging problem in outdoor surveillance systems. In this paper, we propose a new algorithm for rain detection, which is based on joint spatial and wavelet domain features. This approach is robust to the videos with moving objects in the rain. Experimental results demonstrated its better performance in comparison with the existing approaches in the subjective quality.