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arXiv · 2603.10517

UHD Image Deblurring via Autoregressive Flow with Ill-conditioned Constraints

Yucheng Xin·Dawei Zhao·Xiang Chen·Chen Wu·Pu Wang·Dianjie Lu·Guijuan Zhang·Xiuyi Jia·Zhuoran Zheng
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Abstract

Ultra-high-definition (UHD) image deblurring poses significant challenges for UHD restoration methods, which must balance fine-grained detail recovery and practical inference efficiency. Although prominent discriminative and generative methods have achieved remarkable results, a trade-off persists between computational cost and the ability to generate fine-grained detail for UHD image deblurring tasks. To further alleviate these issues, we propose a novel autoregressive flow method for UHD image deblurring with an ill-conditioned constraint. Our core idea is to decompose UHD restoration into a progressive, coarse-to-fine process: at each scale, the sharp estimate is formed by upsampling the previous-scale result and adding a current-scale residual, enabling stable, stage-wise refinement from low to high resolution. We further introduce Flow Matching to model residual generation as a conditional vector field and perform few-step ODE sampling with efficient Euler/Heun solvers, enriching details while keeping inference affordable. Since multi-step generation at UHD can be numerically unstable, we propose an ill-conditioning suppression scheme by imposing condition-number regularization on a feature-induced attention matrix, improving convergence and cross-scale consistency. Our method demonstrates promising performance on blurred images at 4K (3840$\times$2160) or higher resolutions.

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Yucheng Xin, Dawei Zhao, Xiang Chen, Chen Wu, Pu Wang, Dianjie Lu, Guijuan Zhang, Xiuyi Jia, Zhuoran Zheng. 2026-03-11. UHD Image Deblurring via Autoregressive Flow with Ill-conditioned Constraints. https://arxiv.org/abs/2603.10517

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