Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Outdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods

Deraining and Desnowing Algorithm on Adaptive Tolerance and Dual-tree Complex Wavelet Fusion

Severe weather conditions such as rain and snow often reduce the visual perception quality of the video image system, the traditional methods of deraining and desnowing usually rarely consider adaptive parameters. In order to enhance the effect of video deraining and desnowing, this paper proposes a video deraining and desnowing algorithm based on adaptive tolerance and dual-tree complex wavelet. This algorithm can be widely used in security surveillance, military defense, biological monitoring, remote sensing and other fields. First, this paper introduces the main work of the adaptive tolerance method for the video of dynamic scenes. Second, the algorithm of dual-tree complex wavelet fusion is analyzed and introduced. Using principal component analysis fusion rules to process low-frequency sub-bands, the fusion rule of local energy matching is used to process the high-frequency sub-bands. Finally, this paper used various rain and snow videos to verify the validity and superiority of image reconstruction. Experimental results show that the algorithm has achieved good results in improving the image clarity and restoring the image details obscured by raindrops and snows