Uncertainty Aware Multitask Pyramid Vision Transformer For Uav-Based Object Re-Identification

Video super-resolution (VSR), especially continuous-scale VSR, plays a crucial role in improving the quality of satellite video images. 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 between frames at different times. Second, we develop a scale-adaptive feature extraction module (SFEM), which uses the proposed scale-adaptive convolution (SA-Conv) to generate dynamic scale-adaptive 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 different feature 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.