Abstract
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.
Highlights
The target of super-resolution (SR) is to generate a corresponding high-resolution (HR) image or video from its low-resolution (LR) version
As an extension of single image super-resolution (SISR), video super-resolution (VSR) provides a solution to restore the correct content from the degraded video, so that the reconstructed video frames will contain more details with higher clarity
Vanilla SISR approaches can be directly applied to video frames by treating them as single images, abundant detail information available from neighboring frames will be wasted
Summary
The target of super-resolution (SR) is to generate a corresponding high-resolution (HR) image or video from its low-resolution (LR) version. As an extension of single image super-resolution (SISR), video super-resolution (VSR) provides a solution to restore the correct content from the degraded video, so that the reconstructed video frames will contain more details with higher clarity. Such kind of technology with important practical significance can be widely used in many fields such as video surveillance [1], ultra-high definition television [2] and so on. Vanilla SISR approaches can be directly applied to video frames by treating them as single images, abundant detail information available from neighboring frames will be wasted
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