解决真实图像超分中细节丢失与语义偏移问题
Preserve More Details: Mitigating Content Drift in Real-World Image Super-Resolution
- 双路径扩散模型，分别处理细节恢复与语义修正
- 在标准数据集上优于现有方法，细节更清晰、语义更准确
- 适合需要高保真图像重建的研究者与工程师
真实世界图像超分辨率（Real-ISR）旨在从受多种现实退化影响的低质量（LQ）输入中重建高质量（HQ）图像。近期方法利用LQ输入和稳定扩散模型学习的自然图像先验，取得了显著成果。然而，现有方法常忽略LQ输入本身清晰度不足会引发生成图像的内容漂移，表现为视觉细节退化和文本语义偏移，严重影响保真度与感知质量。为此，我们提出FSP-Diff，一种新型单步扩散模型，采用双路径架构：细节条件路径注入结构化细节以恢复精细结构；细节调制语义路径则利用结构化细节优化语义引导，缓解语义偏差。在标准Real-ISR基准上的大量实验表明，FSP-Diff在定量与定性指标上均优于现有单步扩散方法。
原文摘要 · Abstract (English)
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.
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