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

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.