MetaVSR: A Novel Approach to Video Super-Resolution for Arbitrary Magnification

Zixuan Hong, Weipeng Cao, Zhiwu Xu, Zhenru Chen, Xi Tao, Zhong Ming, Chuqing Cao, Liang Zheng

Abstract: Video super-resolution is a pivotal task that involves the recovery of high-resolution video frames from their low-resolution counterparts, possessing a multitude of applications in real-world scenarios. Within the domain of prevailing video super-resolution models, a majority of these models are tailored to specific magnification factors, thereby lacking a cohesive architecture capable of accommodating arbitrary magnifications. In response to this lacuna, this study introduces “MetaVSR”, a novel video super-resolution model devised to handle arbitrary magnifications. This model is structured around three distinct modules: inter-frame alignment, feature extraction, and upsampling. In the inter-frame alignment module, a bidirectional propagation technique is employed to attain the alignment of adjacent frames. The feature extraction module amalgamates superficial and profound video features to enhance the model’s representational prowess. The upsampling module serves to establish a mapping correlation between the desired target resolution and the input provided in lower resolution. An array of empirical findings attests to the efficacy of the proposed MetaVSR model in addressing this challenge.