Heavy snow removal in video based on tensor dimensionality reduction and simultaneous sparse coding

Guofeng Wei; Zhenhong Jia

doi:10.1117/12.3035131

19 July 2024 Heavy snow removal in video based on tensor dimensionality reduction and simultaneous sparse coding

Guofeng Wei, Zhenhong Jia

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132133H (2024) https://doi.org/10.1117/12.3035131
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

We propose a novel snow removal algorithm. First, we make full use of the high-dimensional character- istics of video data and propose a tensor dimensionality reduction-based robust principal component analysis (TDR-RPCA) method to reconstruct clean background, which improves the traditional RPCA’s ability to handle four-dimensional data and solves the problems with artifacts residue. Then a penalty regularization term-based outlier detection method is used to detect moving objects and glob- al large snowflakes. A binary mask containing moving object and snowflakes is obtained. According to the mask, we design an adaptive thresholding strategy to remove snowflakes. Our scheme can effectively remove global snowflakes. Considering the sparse characteristic of snowflakes, tinny snowflakes in front of moving objects can be regarded as noise; hence, we deal with these tiny snowflakes using Gaussian-scale mixture-based simultaneous sparse coding. Experiments show that our method can remove snowflakes in videos captured during heavy snow conditions and eliminate artifact residue, which outperforms other state-of-the-art methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guofeng Wei and Zhenhong Jia "Heavy snow removal in video based on tensor dimensionality reduction and simultaneous sparse coding", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132133H (19 July 2024); https://doi.org/10.1117/12.3035131

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