重新审视基于扩散模型的视频超分辨率：利用对齐特征提供稠密引导
Rethinking Diffusion Model-Based Video Super-Resolution: Leveraging Dense Guidance from Aligned Features
论文指出在特征域而非像素域做相邻帧信息补偿更有效，并给出「上采样分辨率下做warp更能保留高频但收益非单调」的实证结论；做视频复原/时序条件扩散的人可直接复用其光流引导warp模块与特征级时序条件注入方式，替代常见的像素域对齐+拼接。
原文依据 the feature domain is better suited than the pixel domain for information compensation due to its stronger spatial and temporal correlations
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于扩散模型（DM）的视频超分辨率（VSR）方法取得了令人印象深刻的感知质量。然而，现有基于 DM 的 VSR 方法过度优先考虑感知合成，而忽视了来自精确对齐与充分补偿的保真度收益。本文在基于 DM 的 VSR 流程中重新审视相邻视频帧之间对齐与补偿的作用，并揭示了两点关键观察：(a) 由于具有更强的空间与时序相关性，特征域比像素域更适合进行信息补偿；(b) 在上采样后的分辨率上进行warp能更好地保留高频信息，但这一收益未必是单调的。因此，我们提出一种新的、利用对齐特征进行稠密引导的扩散模型用于视频超分辨率（DGAF-VSR），其中包含一个光流引导warp模块（OGWM）以在对齐特征中保持高频细节，以及一个特征级时序条件模块（FTCM）以在特征域中提供稠密引导。在合成与真实世界数据集上的大量实验表明，DGAF-VSR 在 VSR 的关键方面超越了最优方法，包括感知质量（DISTS 降低 35.82%）、保真度（峰值信噪比提升 0.20 dB）以及时序一致性（tLPIPS 降低 30.37%）。
英文原文摘要 1425 字符
Diffusion model (DM) based Video Super-Resolution (VSR) approaches achieve impressive perceptual quality. Diffusion model (DM) based Video Super-Resolution (VSR) approaches achieve impressive perceptual quality. However, existing DM-based VSR methods over-prioritize perceptual synthesis while neglecting fidelity gains from accurate alignment and sufficient compensation. In this paper, within the DM-based VSR pipeline, we revisit the role of alignment and compensation between adjacent video frames and reveal two crucial observations: (a) the feature domain is better suited than the pixel domain for information compensation due to its stronger spatial and temporal correlations, and (b) warping at an upscaled resolution better preserves high-frequency information, but this benefit is not necessarily monotonic. Therefore, we propose a novel Densely Guided diffusion model with Aligned Features for Video Super-Resolution (DGAF-VSR), with an Optical Guided Warping Module (OGWM) to maintain high-frequency details in the aligned features and a Feature-wise Temporal Condition Module (FTCM) to deliver dense guidance in the feature domain. Extensive experiments on synthetic and real-world datasets demonstrate that DGAF-VSR surpasses state-of-the-art methods in key aspects of VSR, including perceptual quality (35.82% DISTS reduction), fidelity (0.20 dB PSNR gain), and temporal consistency (30.37% tLPIPS reduction).