Depth prior-based stable tensor decomposition for video snow removal

作者：Yongji Li, Luping Wang, Zhenhong Jia, Jie Yang, Nikola Kasabov · 发表于：Displays · 年份：2024 · DOI：10.1016/j.displa.2024.102733 · 被引用次数：9 · 研究领域：Image Enhancement Techniques、Advanced Image Processing Techniques、Advanced Vision and Imaging

Heavy snow seriously degrades the performance of outdoor computer vision systems. Near- and far-field snowflakes in heavy snow videos exhibit distinctly disparate physical properties. To address this issue, this research proposes a video desnowing model that utilizes stable tensor decomposition with snow depth prior information. Initially, the depths of snowflakes are transformed by their speeds calculated from the optical flow field of snowfall. Next, the noise level in backgrounds is available by dense snow. Finally, inspired by biomimicry , the snow on moving objects (MOs) is removed by the adaptive mimesis region of interest (AM-ROI). In contrast to previous tensor decomposition (TD), the introduction of a noise term enhances the stability of depth prior-based stable tensor decomposition (DP-STD) in addressing heavy snowfall. This approach enables more efficient and accurate restoration of the underlying nonsnow structure. Both synthetic and real snowfall experimental results show that our proposed desnowing model is more effective than the current SOTA algorithm in removing heavy snow.