Video Super-Resolution with Long-Term Self-Exemplars

• URL: http://arxiv.org/abs/2106.12778v1
• Date: Thu, 24 Jun 2021 06:07:13 GMT
• Title: Video Super-Resolution with Long-Term Self-Exemplars
• Authors: Guotao Meng, Yue Wu, Sijin Li, Qifeng Chen
• Abstract summary: We propose a video super-resolution method with long-term cross-scale aggregation. Our model also consists of a multi-reference alignment module to fuse the features derived from similar patches. To evaluate our proposed method, we conduct extensive experiments on our collected CarCam dataset and the Open dataset.
• Score: 38.81851816697984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing video super-resolution methods often utilize a few neighboring frames to generate a higher-resolution image for each frame. However, the redundant information between distant frames has not been fully exploited in these methods: corresponding patches of the same instance appear across distant frames at different scales. Based on this observation, we propose a video super-resolution method with long-term cross-scale aggregation that leverages similar patches (self-exemplars) across distant frames. Our model also consists of a multi-reference alignment module to fuse the features derived from similar patches: we fuse the features of distant references to perform high-quality super-resolution. We also propose a novel and practical training strategy for referenced-based super-resolution. To evaluate the performance of our proposed method, we conduct extensive experiments on our collected CarCam dataset and the Waymo Open dataset, and the results demonstrate our method outperforms state-of-the-art methods. Our source code will be publicly available.

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