Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super Resolution
The paper presents a new framework called ASASR for image super-resolution that addresses the limitations of existing generative methods. It focuses on aligning the generative flow with the natural image manifold by using a Sobolev-induced Riemannian geometry. The results indicate that ASASR significantly improves spectral consistency and structural fidelity compared to leading generative baselines.
- ▪ASASR aims to overcome the spectral misalignment in image super-resolution.
- ▪The framework utilizes a parametric adversary based on the Riesz Representation Theorem.
- ▪Extensive evaluations show that ASASR outperforms existing generative methods in preserving image quality.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23264 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.23264 (cs) [Submitted on 22 May 2026] Title:Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super Resolution Authors:Hongbo Wang, Huaibo Huang, Pin Wang, Jinhua Hao, Chao Zhou, Ran He View a PDF of the paper titled Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super Resolution, by Hongbo Wang and 5 other authors View PDF HTML (experimental) Abstract: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.
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