Abstract
Real-World Image Super-Resolution (Real-ISR) aims to recover high-quality images from low-quality inputs degraded by unknown and complex real-world factors. Real-world scenarios involve diverse and coupled degradations, making it necessary to provide diffusion models with richer and more informative guidance. However, existing methods often assume known degradation severity and rely on CLIP text encoders that cannot capture numerical severity, limiting their generalization ability. To address this, we propose \textbf{HD-CLIP} (\textbf{H}ierarchical \textbf{D}egradation CLIP), which decomposes a low-quality image into a semantic embedding and an ordinal degradation embedding that captures ordered relationships and allows interpolation across unseen levels. Furthermore, we integrated it into diffusion models via classifier-free guidance (CFG) and proposed classifier-free projection guidance (CFPG). HD-CLIP leverages semantic cues to guide generative restoration while using degradation cues to suppress undesired hallucinations and artifacts. As a \textbf{plug-and-play module}, HD-CLIP can be seamlessly integrated into various super-resolution frameworks without training, significantly improving detail fidelity and perceptual realism across diverse real-world datasets.
Abstract (translated)
现实世界图像超分辨率（Real-ISR）旨在从由未知且复杂的现实因素降级的低质量输入中恢复高质量的图像。在实际场景中，存在多样化的和耦合的退化情况，这就需要提供给扩散模型更丰富和更有信息量的指导。然而，现有的方法往往假设已知的退化程度，并依赖于CLIP文本编码器（这些编码器不能捕捉数值上的严重程度），这限制了它们的泛化能力。 为了应对这一挑战，我们提出了一种名为**HD-CLIP**（**H**ierarchical **D**egradation CLIP）的方法。该方法将低质量图像分解为一个语义嵌入和一个序数退化嵌入，后者可以捕获有序关系，并允许在未见过的水平之间进行插值。此外，我们将这种方法整合到扩散模型中，通过无分类器指导（CFG）提出了一种新的引导方式——无分类器投影指导（CFPG）。HD-CLIP利用语义线索来引导生成性恢复过程，同时使用退化线索来抑制不需要的幻觉和伪影。 作为**即插即用模块**，HD-CLIP可以无缝地集成到各种超分辨率框架中，而无需进行额外训练。这显著提高了在多种现实世界数据集上的细节保真度和感知逼真性。
URL
https://arxiv.org/abs/2512.10340
