用隐式神经表示实现任意缩放视频修复，还零样本去噪
Implicit Neural Representation for Video Restoration
- 基于分层时空纹理编码与多分辨率哈希编码，实现灵活缩放
- 仅在×4训练，测试时可处理任意未知缩放倍数和噪声输入
- 无需重训练即可实现超清还原与去噪，适合通用视频修复场景
高分辨率视频在众多计算机视觉应用中至关重要。尽管现有视频修复方法能通过利用帧间时序信息显著提升画质，但通常仅针对固定上采样倍数训练，难以应对超出训练分布的缩放或退化。本文提出基于隐式神经表示（INRs）的视频修复新方法——VR-INR，仅在×4上采样因子下训练，却能在测试时有效泛化至任意未见过的超分辨率尺度。值得注意的是，该方法在从未接触过噪声数据的情况下，仍可实现零样本去噪。我们的方法采用分层时空纹理编码框架结合多分辨率隐式哈希编码，能够从低分辨率输入自适应解码出任意放大倍数下的高分辨率且去噪的帧。实验结果表明，VR-INR在未见尺度和噪声条件下持续保持高质量重建，在清晰度、细节保留和去噪效果方面显著优于现有最优方法。
原文摘要 · Abstract (English)
High-resolution (HR) videos play a crucial role in many computer vision applications. Although existing video restoration (VR) methods can significantly enhance video quality by exploiting temporal information across video frames, they are typically trained for fixed upscaling factors and lack the flexibility to handle scales or degradations beyond their training distribution. In this paper, we introduce VR-INR, a novel video restoration approach based on Implicit Neural Representations (INRs) that is trained only on a single upscaling factor ($\times 4$) but generalizes effectively to arbitrary, unseen super-resolution scales at test time. Notably, VR-INR also performs zero-shot denoising on noisy input, despite never having seen noisy data during training. Our method employs a hierarchical spatial-temporal-texture encoding framework coupled with multi-resolution implicit hash encoding, enabling adaptive decoding of high-resolution and noise-suppressed frames from low-resolution inputs at any desired magnification. Experimental results show that VR-INR consistently maintains high-quality reconstructions at unseen scales and noise during training, significantly outperforming state-of-the-art approaches in sharpness, detail preservation, and denoising efficacy.
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