Abstract
Diffusion-based approaches have recently driven remarkable progress in real-world image super-resolution (SR). However, existing methods still struggle to simultaneously preserve fine details and ensure high-fidelity reconstruction, often resulting in suboptimal visual quality. In this paper, we propose FiDeSR, a high-fidelity and detail-preserving one-step diffusion super-resolution framework. During training, we introduce a detail-aware weighting strategy that adaptively emphasizes regions where the model exhibits higher prediction errors. During inference, low- and high-frequency adaptive enhancers further refine the reconstruction without requiring model retraining, enabling flexible enhancement control. To further improve the reconstruction accuracy, FiDeSR incorporates a residual-in-residual noise refinement, which corrects prediction errors in the diffusion noise and enhances fine detail recovery. FiDeSR achieves superior real-world SR performance compared to existing diffusion-based methods, producing outputs with both high perceptual quality and faithful content restoration. The source code will be released at: this https URL.
Abstract (translated)
基于扩散的方法最近在真实世界的图像超分辨率（SR）领域取得了显著的进步。然而，现有的方法仍然难以同时保留细节并确保高保真度重建，在视觉质量上常常不尽如人意。在这篇论文中，我们提出了FiDeSR，这是一种高度保真且能保持细节的一步式扩散超分辨率框架。 在训练阶段，我们引入了一种感知细节的加权策略，该策略能够自适应地强调模型预测误差较高的区域。在推理阶段，低频和高频自适应增强器进一步细化重建结果，而无需重新训练模型，从而实现灵活的增强控制。为了进一步提高重建精度，FiDeSR集成了残差内残差噪声精炼机制，这种机制可以修正扩散噪声中的预测错误，并提升细部恢复能力。 与现有的基于扩散的方法相比，FiDeSR在真实世界的超分辨率性能方面表现出色，能够生成既具有高感知质量又忠实还原内容的输出。源代码将在以下链接发布：this https URL.
URL
https://arxiv.org/abs/2603.02692
