Abstract:

Under snowy conditions, snowflakes can obstruct video surveillance systems, preventing the capture of important scenery information and drastically reducing the quality of acquired video images. This interference can also strongly affect advanced image processing techniques such as subsequent target detection and recognition. The existing methods for removing snow from video images commonly suffer from drawbacks such as unstable snow removal performance and long computational time. To address this issue, firstly, the advantages of tensors in fully capturing spatial location information within video images were leveraged. By combining a low-rank tensor decomposition model with three-dimensional total variation regularization, the snow-contaminated surveillance video was decomposed into a static background layer and a moving foreground layer. Then, based on the non-subsampled shearlet transform and mathematical morphology filtering methods, the moving foreground layer was further decomposed into a moving object layer and a snow layer. Finally, the static background layer and moving object layer were reconstructed to obtain snow-free video images. The experimental results demonstrated the effectiveness of this approach in removing snowflake interference from video images while clearly retaining scene edge information. Moreover, the proposed method outperforms existing state-of-the-art algorithms in terms of processing efficacy and operational efficiency.

Key words: low-rank tensor decomposition, non-subsampled shearlet transform, snow removal in video, mathematical morphology filtering