Deformable Alignment And Scale-Adaptive Feature Extraction Network For Continuous-Scale Satellite Video Super-Resolution

Abstract

Video super-resolution (VSR), especially continuous-scale VSR, plays a crucial role in improving the quality of satellite video. Continuous-scale VSR aims to use a single model to process arbitrary (integer or non-integer) scale factors, which is conducive to meeting the needs of video images transmission with different compression ratios and arbitrarily zooming by rolling the mouse wheel. In this article, we propose a novel network to achieve continuous-scale satellite VSR (CAVSR). Specifically, first, we propose a time-series-aware dynamic routing deformable alignment module (TDAM) for feature alignment. Second, we develop a scale-adaptive feature extraction module (SFEM), which uses the proposed scale-adaptive convolution (SA-Conv) to dynamically generate different filters based on the input scale information. Finally, we design a global implicit function feature-adaptive walk continuous-scale upsampling module (GFCUM), which can perform feature-adaptive walks according to the input features with different scale information and finally complete the continuous-scale mapping from coordinates to pixel values. Experimental results have demonstrated the CAVSR has superior reconstruction performance.