arXiv:2605.23264v1 Announce Type: cross 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. 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-induc
The paper addresses a current limitation in generative AI for image super-resolution related to spectral misalignment and the distinction of authentic details from hallucinations.
This research provides a theoretically grounded framework to improve the fidelity and detail of AI-generated images, which has implications across various AI applications requiring high-quality visual outputs.
The proposed ASASR framework changes how generative flows in image super-resolution are approached, moving from isotropic objectives to a Sobolev-induced geometric alignment for better detail preservation.
- · AI researchers
- · Generative AI developers
- · Creative industries relying on image upscaling
- · Computer Vision sector
- · Generative models producing hallucinated details
- · Image super-resolution techniques lacking fidelity
Improved quality and authenticity in AI-generated and enhanced images will be observed.
The enhanced fidelity could accelerate the adoption of generative AI in sensitive applications like medical imaging or forensics.
Higher quality synthetic data could further refine AI model training, creating a virtuous cycle of improvement across various AI domains.
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Read at arXiv cs.AI