噪声着色：对抗性索博列夫对齐实现忠实图像超分辨率

摘要

在图像超分辨率（SR）中，生成先验往往以牺牲忠实重建为代价，我们将这一局限归因于各向同性目标函数与内在自然图像流形之间的根本性频谱失配。尽管直接偏好优化为对齐提供了路径，但其依赖频谱平坦的高斯噪声，无法区分真实高频细节与幻觉。为弥合这一几何鸿沟，我们提出ASASR——一个具有理论基础框架的方法，通过显式着色噪声转移核以镜像自然的频谱衰减，从而将生成流重塑为Sobolev诱导的黎曼几何。为驱动这一几何对齐，我们集成了一个基于Riesz表示定理的参数化对抗机制，该机制合成针对性的负样本，其等价于最坏情况下的Sobolev梯度，从而沿可能结构失效的切空间引导优化。大量评估表明，ASASR在保持频谱一致性和结构保真度方面优于主流生成基线方法，提供了有效缓解伪影的稳健解决方案。

English

Generative priors in Image Super-Resolution (SR) often compromise faithful restoration, we attribute this limitation to a fundamental spectral misalignment between isotropic objectives and the intrinsic natural image manifold. While Direct Preference Optimization offers a path to alignment, its reliance on spectrally flat Gaussian noise fails to distinguish authentic high-frequency details from hallucinations. To bridge this geometric gap, we propose ASASR, a theoretically grounded framework that recasts the generative flow into a Sobolev-induced Riemannian geometry by explicitly coloring the noise transition kernel to mirror natural spectral decay. Driving this geometric alignment, we integrate a parametric adversary grounded in the Riesz Representation Theorem, which synthesizes targeted negative samples equivalent to worst-case Sobolev gradients to direct optimization along the tangent space of plausible structural failures. Extensive evaluations demonstrate that ASASR outperforms leading generative baselines, particularly in preserving spectral consistency and structural fidelity, offering a robust solution that effectively mitigates artifacts.