Multi-Video Super-Resolution: Spatiotemporal Fusion for Sparse Camera Array

A sparse camera array captures multiple images of a scene within the same spatial plane, enabling super-resolution reconstruction. However, existing methods often fail to fully exploit time as an additional dimension for enhanced information acquisition. Even when temporal and spatial observations are collected simultaneously, their individual contributions are often conflated. Analysis of the system's imaging model reveals that the spatiotemporal camera system, integrating a camera array with video sequences, holds greater potential for degradation recovery. Based on these insights, we propose a novel multi-video super-resolution network for spatiotemporal information fusion. Guided by explicit physical dimensional orientation, the network effectively integrates spatial information and propagates it along the temporal dimension. By utilizing diverse and informative spatiotemporal sampling, our method more readily addresses challenges arising from ill-posed mapping matrices during reconstruction. Experimental results on both synthetic and real-world datasets show that the components of our network, with information fully propagated and spatiotemporally fused, work synergistically to enhance super-resolution performance, providing substantial improvements over state-of-the-art methods. We believe our study can inspire innovations for future super-resolution tasks by optimizing information acquisition and utilization.