The video super-resolution (VSR) task aims to restore a high-resolution (HR)
video frame by using its corresponding low-resolution (LR) frame and multiple
neighboring frames. At present, many deep learning-based VSR methods rely on
optical flow to perform frame alignment. The final recovery results will be
greatly affected by the accuracy of optical flow. However, optical flow
estimation cannot be completely accurate, and there are always some errors. In
this paper, we propose a novel deformable non-local network (DNLN) which is a
non-optical-flow-based method. Specifically, we apply the deformable
convolution and improve its ability of adaptive alignment at the feature level.
Furthermore, we utilize a non-local structure to capture the global correlation
between the reference frame and the aligned neighboring frames, and
simultaneously enhance desired fine details in the aligned frames. To
reconstruct the final high-quality HR video frames, we use residual in residual
dense blocks to take full advantage of the hierarchical features. Experimental
results on benchmark datasets demonstrate that the proposed DNLN can achieve
state-of-the-art performance on VSR task.

本文提出了一种新颖的变形非局部网络（DNLN），其是一种非基于光流的方法，旨在通过采用变形卷积并在特征级别提高其自适应对齐能力来恢复高质量的视频超分辨率帧。通过利用非局部结构捕捉参考帧和对齐的相邻帧之间的全局相关性并同时增强对齐帧中的所需细节，DNLN 能够实现 VSR 任务的最先进性能。