Bridging Fidelity-Reality with Controllable One-Step Diffusion for Image Super-Resolution

• URL: http://arxiv.org/abs/2512.14061v1
• Date: Tue, 16 Dec 2025 03:56:02 GMT
• Title: Bridging Fidelity-Reality with Controllable One-Step Diffusion for Image Super-Resolution
• Authors: Hao Chen, Junyang Chen, Jinshan Pan, Jiangxin Dong,
• Abstract summary: CODSR is a controllable one-step diffusion network for image super-resolution.<n>We propose an LQ-guided feature modulation module to provide high-fidelity conditioning for the diffusion process.<n>We develop a region-adaptive generative prior activation method to effectively enhance perceptual richness.
• Score: 59.71803719801537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent diffusion-based one-step methods have shown remarkable progress in the field of image super-resolution, yet they remain constrained by three critical limitations: (1) inferior fidelity performance caused by the information loss from compression encoding of low-quality (LQ) inputs; (2) insufficient region-discriminative activation of generative priors; (3) misalignment between text prompts and their corresponding semantic regions. To address these limitations, we propose CODSR, a controllable one-step diffusion network for image super-resolution. First, we propose an LQ-guided feature modulation module that leverages original uncompressed information from LQ inputs to provide high-fidelity conditioning for the diffusion process. We then develop a region-adaptive generative prior activation method to effectively enhance perceptual richness without sacrificing local structural fidelity. Finally, we employ a text-matching guidance strategy to fully harness the conditioning potential of text prompts. Extensive experiments demonstrate that CODSR achieves superior perceptual quality and competitive fidelity compared with state-of-the-art methods with efficient one-step inference.

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