提出隐式核模型，提升未知退化下的视频超分辨率效果
Blind Video Super-Resolution based on Implicit Kernels
- 用隐式神经表示构建多尺度核字典，捕捉时空变化退化
- 在三个数据集上比最优基线高0.59 dB的PSNR
- 适合处理复杂退化场景的视频增强任务
盲视频超分辨率（BVSR）旨在未知退化条件下从低分辨率视频生成高分辨率视频。现有方法通常假设退化核在帧内或整段视频中保持空间不变，未能考虑视频中潜在的时空变化退化，导致性能受限。为此，本文提出基于隐式核的新型BVSR模型——BVSR-IK，通过隐式神经表示参数化多尺度核字典，并设计新型循环Transformer预测系数权重，实现帧校正与特征对齐中的精准滤波。实验表明，相比四种先进BVSR模型，在三个常用数据集上，BVSR-IK性能超越第二佳方法FMA-Net达0.59 dB（PSNR）。代码将开源。
原文摘要 · Abstract (English)
Blind video super-resolution (BVSR) is a low-level vision task which aims to generate high-resolution videos from low-resolution counterparts in unknown degradation scenarios. Existing approaches typically predict blur kernels that are spatially invariant in each video frame or even the entire video. These methods do not consider potential spatio-temporal varying degradations in videos, resulting in suboptimal BVSR performance. In this context, we propose a novel BVSR model based on Implicit Kernels, BVSR-IK, which constructs a multi-scale kernel dictionary parameterized by implicit neural representations. It also employs a newly designed recurrent Transformer to predict the coefficient weights for accurate filtering in both frame correction and feature alignment. Experimental results have demonstrated the effectiveness of the proposed BVSR-IK, when compared with four state-of-the-art BVSR models on three commonly used datasets, with BVSR-IK outperforming the second best approach, FMA-Net, by up to 0.59 dB in PSNR. Source code will be available at https://github.com/QZ1-boy/BVSR-IK.
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