用一步扩散模型提升超分辨率，更符合人眼感知。
Hero-SR: One-Step Diffusion for Super-Resolution with Human Perception Priors
- 引入动态时间步模块，自适应选择最佳生成步骤。
- 结合图文双模态监督，提升语义一致性和自然感。
- 适合需要真实感超分的图像修复与生成任务。
由于扩散模型的强大先验，近期方法在真实世界超分辨率（Real-SR）中展现出潜力。然而，在重度退化和复杂输入条件下，实现语义一致性与人眼感知自然性仍具挑战。为此，我们提出Hero-SR，一种基于一步扩散的超分辨率框架，显式融入人类感知先验。该框架包含两个新模块：动态时间步模块（DTSM），可自适应选择最优扩散步数以灵活匹配人眼感知标准；开放世界多模态监督（OWMS），通过CLIP融合图像与文本域指导，提升语义一致性和感知自然性。得益于这些设计，Hero-SR生成的高分辨率图像不仅保留精细细节，还符合人类感知偏好。大量实验表明，Hero-SR在Real-SR任务上达到当前最优性能。代码将在论文录用后公开。
原文摘要 · Abstract (English)
Owing to the robust priors of diffusion models, recent approaches have shown promise in addressing real-world super-resolution (Real-SR). However, achieving semantic consistency and perceptual naturalness to meet human perception demands remains difficult, especially under conditions of heavy degradation and varied input complexities. To tackle this, we propose Hero-SR, a one-step diffusion-based SR framework explicitly designed with human perception priors. Hero-SR consists of two novel modules: the Dynamic Time-Step Module (DTSM), which adaptively selects optimal diffusion steps for flexibly meeting human perceptual standards, and the Open-World Multi-modality Supervision (OWMS), which integrates guidance from both image and text domains through CLIP to improve semantic consistency and perceptual naturalness. Through these modules, Hero-SR generates high-resolution images that not only preserve intricate details but also reflect human perceptual preferences. Extensive experiments validate that Hero-SR achieves state-of-the-art performance in Real-SR. The code will be publicly available upon paper acceptance.
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