Disentangle Propagation and Restoration for Efficient Video Recovery

We propose the first framework for accelerating video recovery, which aims to efficiently recover high-quality videos from degraded inputs affected by various deteriorative factors. Although current video recovery methods have achieved excellent performance, their significant computational overhead limits their widespread application. To address this, we present a pioneering study on explicitly disentangling temporal and spatial redundant computation by decomposing the input frame into propagation and restoration regions, thereby achieving significant computational reduction. Specifically, we leverage contrastive learning to learn degradation-invariant features, which overcomes the disturbance of deteriorative factors and enables accurate disentanglement. For the propagation region, we introduce a split-fusion block to address inter-frame variations, efficiently generating high-quality output at a low cost and significantly reducing temporal redundant computation. For the restoration region, we propose an efficient adaptive halting mechanism that requires few extra parameters and can adaptively halt the patch processing, considerably reducing spatial redundant computation. Furthermore, we design patch-adaptive prior regularization to boost efficiency and performance. Our proposed method achieves outstanding results on various video recovery tasks, such as video denoising, video deraining, video dehazing, and video super-resolution, with a 50% 60% reduction in GMAC over the state-of-the-art video recovery methods while maintaining comparable performance.