Multi-scale feature fusion network with spatial-temporal alignment for video denoising

Yushan Lv; Di Wu; Yuhang Li; Youdong Ding

doi:10.1117/12.2667325

22 February 2023 Multi-scale feature fusion network with spatial-temporal alignment for video denoising

Yushan Lv, Di Wu, Yuhang Li, Youdong Ding

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 125871D (2023) https://doi.org/10.1117/12.2667325
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

Most existing video denoising methods based on the PatchMatch algorithm and optical flow estimation often lead to artifacts blurring and poor denoising effect on scale-varying data. To tackle these issues, we propose a multi-scale feature fusion network based on different pyramid blocks and adaptive spatial-channel attention, which enables to effectively extract multi-scale feature information from noisy video data. Furthermore, we develop a spatial-temporal alignment module with deformable convolution to align the implicit features and reduce blurring artifacts. The results show that the proposed method outperforms the state-of-the-art algorithms in visual and objective quality metrics on the public datasets DAVIS and Set8.

Citation Download Citation

Yushan Lv, Di Wu, Yuhang Li, and Youdong Ding "Multi-scale feature fusion network with spatial-temporal alignment for video denoising", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 125871D (22 February 2023); https://doi.org/10.1117/12.2667325

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