In recent years, single-frame image super-resolution (SR) has become more
realistic by considering the zooming effect and using real-world short- and
long-focus image pairs. In this paper, we further investigate the feasibility
of applying realistic multi-frame clips to enhance zoom quality via
spatio-temporal information coupling. Specifically, we first built a real-world
video benchmark, VideoRAW, by a synchronized co-axis optical system. The
dataset contains paired short-focus raw and long-focus sRGB videos of different
dynamic scenes. Based on VideoRAW, we then presented a Spatio-Temporal Coupling
Loss, termed as STCL. The proposed STCL is intended for better utilization of
information from paired and adjacent frames to align and fuse features both
temporally and spatially at the feature level. The outperformed experimental
results obtained in different zoom scenarios demonstrate the superiority of
integrating real-world video dataset and STCL into existing SR models for zoom
quality enhancement, and reveal that the proposed method can serve as an
advanced and viable tool for video zoom.

本文研究将真实多帧片段应用于超分辨率以提高缩放质量，并在可称为 Spatio-Temporal Coupling Loss 的损失函数中提出更好地利用配对和相邻帧信息的特征级别的空时信息耦合途径，以整合现有 SR 模型中的真实世界视频数据集和 STCL 以获取优越的实验结果。