发表机构
Xiaomi Corporation（小米公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对真实世界图像超分辨率中因低质量输入导致的内容漂移问题，提出双路径架构的新型单步扩散模型FSP-Diff，在标准基准上优于现有单步扩散方法。
AI 中文摘要
真实世界图像超分辨率（Real-ISR）旨在从受多样真实世界退化影响的低质量（LQ）输入中重建高质量（HQ）图像。近期进展利用LQ输入和Stable Diffusion模型学习到的自然图像先验取得了令人印象深刻的结果。然而，现有方法常因LQ输入清晰度不足，不可避免地在生成的HQ图像中引发内容漂移，主要表现为视觉细节退化和文本语义偏移，严重损害保真度和感知质量。为应对这一挑战，我们提出FSP-Diff，一种具有双路径架构的新型单步扩散模型。该架构包含用于注入结构化细节以恢复精细结构的细节条件路径，以及利用结构化细节优化语义引导以缓解语义偏差的细节调制语义路径。在标准Real-ISR基准上的大量实验表明，FSP-Diff在定量和定性指标上均优于现有单步扩散方法。
英文摘要
Real-world image super-resolution (Real-ISR) aims to reconstruct high-quality (HQ) images from low-quality (LQ) inputs subject to diverse real-world degradations. Recent advances have leveraged the LQ inputs and natural image priors learned by Stable Diffusion models to achieve impressive results. However, existing methods often overlook insufficient clarity of LQ inputs inevitably induce content drift in the generated HQ images. This manifests primarily as visual detail degradation and textual semantic shift, severely compromising both fidelity and perceptual quality. To address this challenge, we propose FSP-Diff, a novel one-step diffusion model featuring a dual-pathway architecture. This architecture comprises a Detail-Conditioned Pathway for injecting structured details to recover fine structures, and a Detail-Modulated Semantic Pathway that refines semantic guidance using structured details to mitigate semantic deviations. Extensive experiments on standard Real-ISR benchmarks demonstrate that FSP-Diff surpasses existing one-step diffusion methods in both quantitative and qualitative metrics.
CommentsAccepted to ACM MM 2026. This is the author's accepted version. The definitive version is published in the Proceedings of ACM MM 2026
