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.
Abstract (translated)
最近基于扩散的一步法在图像超分辨率领域取得了显著进步，但仍受三个关键限制的制约：(1) 由于对低质量（LQ）输入进行压缩编码导致的信息丢失而产生的较低保真度性能；(2) 生成功能未能充分激活区域识别功能；(3) 文本提示与其相应语义区域之间的不匹配。为解决这些问题，我们提出了CODSR，一种用于图像超分辨率的可控一步扩散网络。首先，我们提出了一种LQ引导特征调节模块，该模块利用来自LQ输入的原始未压缩信息来为扩散过程提供高保真度条件设置。然后，我们开发了一种区域自适应生成功能激活方法，以有效增强感知丰富性而不牺牲局部结构保真度。最后，我们采用一种文本匹配指导策略，充分利用文本提示的调节潜力。广泛的实验表明，CODSR在视觉质量和保真度方面优于最先进的方法，并且其一步推理效率非常高。
URL
https://arxiv.org/abs/2512.14061
