Abstract
Video super-resolution (VSR) methods align and fuse consecutive low-resolution frames to generate high-resolution frames. One of the main difficulties for VSR process is that video contains various motions, and the accuracy of motion estimation greatly affects the quality of video restoration. Diverse receptive fields in temporal and spatial dimensions have the potential to adapt to various motions, which is rarely paid attention in most known VSR methods. In this paper, we propose to provide adaptive receptive fields for VSR model. Firstly, we design a multi-kernel 3D convolution network and integrate it with a multi-kernel deformable convolution network for motion estimation and multiple frames alignment. Secondly, we propose a 2D multi-kernel convolution framework to improve texture restoration quality. Our experimental results show that the proposed framework outperforms the state-of-the-art VSR methods.
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