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• W4385804941 abstract "Video super-resolution (VSR) aiming to reconstruct a high-resolution (HR) video from its low-resolution (LR) counterpart has made tremendous progress in recent years. However, it remains challenging to deploy existing VSR methods to real-world data with complex degradations. On the one hand, there are few well-aligned real-world VSR datasets, especially with large super-resolution scale factors, which limits the development of real-world VSR tasks. On the other hand, alignment algorithms in existing VSR methods perform poorly for real-world videos, leading to unsatisfactory results. As an attempt to address the aforementioned issues, we build a real-world ×4 VSR dataset, namely MVSR4×, where low- and high-resolution videos are captured with different focal length lenses of a smartphone, respectively. Moreover, we propose an effective alignment method for real-world VSR, namely EAVSR. EAVSR takes the proposed multi-layer adaptive spatial transform network (MultiAdaSTN) to refine the offsets provided by the pre-trained optical flow estimation network. Experimental results on RealVSR and MVSR4× datasets show the effectiveness and practicality of our method, and we achieve state-of-the-art performance in real-world VSR task. The dataset and code will be available at https://github.com/HITRainer/EAVSR." @default.
• W4385804941 created "2023-08-15" @default.
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• W4385804941 date "2023-06-01" @default.
• W4385804941 modified "2023-09-26" @default.
• W4385804941 title "Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution" @default.
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• W4385804941 doi "https://doi.org/10.1109/cvprw59228.2023.00124" @default.
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