Abstract
Diffusion-based image super-resolution (ISR) has shown strong potential, but it still struggles in real-world scenarios where degradations are unknown and spatially non-uniform, often resulting in lost details or visual artifacts. To address this challenge, we propose a novel super-resolution diffusion model, QUSR, which integrates a Quality-Aware Prior (QAP) with an Uncertainty-Guided Noise Generation (UNG) module. The UNG module adaptively adjusts the noise injection intensity, applying stronger perturbations to high-uncertainty regions (e.g., edges and textures) to reconstruct complex details, while minimizing noise in low-uncertainty regions (e.g., flat areas) to preserve original information. Concurrently, the QAP leverages an advanced Multimodal Large Language Model (MLLM) to generate reliable quality descriptions, providing an effective and interpretable quality prior for the restoration process. Experimental results confirm that QUSR can produce high-fidelity and high-realism images in real-world scenarios. The source code is available at this https URL.
Abstract (translated)
基于扩散的图像超分辨率（ISR）展现了强大的潜力，但在现实场景中却面临挑战，尤其是在退化情况未知且空间非均匀的情况下，往往会导致细节丢失或视觉伪影。为了解决这一问题，我们提出了一种新的超分辨率扩散模型QUSR，它集成了质量感知先验(QAP)和不确定性引导噪声生成(UNG)模块。UNG模块能够自适应地调整噪声注入强度，在高不确定区域（如边缘和纹理）应用更强的扰动以重建复杂细节，而在低不确定区域（如平坦区域）则尽量减少噪声以保留原有信息。同时，QAP利用先进的多模态大型语言模型(MLLM)生成可靠的质量描述，为恢复过程提供有效且可解释的质量先验。实验结果证实了QUSR能够在现实场景中产生高保真度和高真实感的图像。源代码可在该网址获得（原链接未提供具体URL，请访问相关研究论文或官方发布渠道获取）。
URL
https://arxiv.org/abs/2603.09125
