Li Yongji, Wu Rui, Jia Zhenhong, Yang Jie, Kasabov Nikola
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China.
Sensors (Basel). 2021 Nov 16;21(22):7610. doi: 10.3390/s21227610.
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.
室外视觉传感系统常常在恶劣天气条件下（如雪和雨）面临困难，这对现有的视频除雪和除雨方法构成了巨大挑战。在本文中，我们提出了一种新颖的视频除雪和除雨模型，该模型利用移动物体的显著信息来解决这个问题。首先，我们通过低秩张量分解去除视频中的雪和雨，这充分利用了彩色视频三个通道之间的空间位置信息和相关性。其次，由于现有算法常常将稀疏雪花和雨痕视为移动物体，本文将显著信息注入到移动物体检测中，减少了移动物体的误报和漏报。同时，利用特征点匹配挖掘连续帧中移动物体的冗余信息，并提出了一种时空域双重自适应最小滤波算法来去除移动物体前方的雪和雨。定性和定量实验结果均表明，所提出的算法比其他现有最先进的除雪和除雨方法更具竞争力。
