3D Deformable Kernels for Video super-resolution
Jingsong Zhou, Rushi Lan, Xiaoqing Wang, Cheng Pang, Xiaonan Luo · 2022
Video super-resolution are drawing increasing attention in the computer vision community. Temporal modeling is crucial for video super-resolution. A challenge for video super-resolution to fully mining temporal-spatial information in video sequence. In this work, we propose 3D deformable kernels for video super-resolution (DK3Dnet). Specifically, we introduce 3D deformable kernels (DK3D) to integrate deformable convolution with 3D convolution to enhance spatio-temporal modeling capability. To enhance the quality of subsequent restoration. we use a Temporal and Spatial Attention fusion module (TSA fusion), in which attention is applied both temporally and spatially. Finally, we use channel-wise attention residual block (CARB) to enhance the quality of video frame in DK3Dnet reconstruction module. Experimental results show that DK3Dnet can exploiting spatio-temporal information to improve the performance of video super-resolution.