FiDeSR: High-Fidelity and Detail-Preserving One-Step Diffusion Super-Resolution

• URL: http://arxiv.org/abs/2603.02692v1
• Date: Tue, 03 Mar 2026 07:34:49 GMT
• Title: FiDeSR: High-Fidelity and Detail-Preserving One-Step Diffusion Super-Resolution
• Authors: Aro Kim, Myeongjin Jang, Chaewon Moon, Youngjin Shin, Jinwoo Jeong, Sang-hyo Park,
• Abstract summary: We propose FiDeSR, a high-fidelity and detail-preserving one-step diffusion super-resolution framework.<n>During training, we introduce a detail-aware weighting strategy that adaptively emphasizes regions where the model exhibits higher prediction errors.<n>During inference, low- and high-frequency adaptive enhancers further refine the reconstruction without requiring model retraining.
• Score: 11.03986460753769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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: https://github.com/Ar0Kim/FiDeSR.

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