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.
Abstract (translated)
超高清（UHD）图像去模糊对恢复方法提出了重大挑战，这些方法必须在精细细节恢复和实用推理效率之间取得平衡。尽管一些显著的判别性和生成性方法已经取得了令人瞩目的成果，在计算成本与生成超高清图像去模糊任务中细粒度细节的能力之间仍存在权衡问题。为了解决这些问题，我们提出了一种新的自回归流方法用于带有病态条件约束的UHD图像去模糊。 我们的核心思想是将UHD恢复过程分解成一个渐进式的、由粗到细的过程：在每一尺度下，锐化估计通过上采样前一尺度的结果并添加当前尺度的残差来形成，从而实现从低分辨率到高分辨率的稳定阶段式细化。我们进一步引入流匹配（Flow Matching）以将残差生成建模为条件向量场，并采用高效的欧拉/赫恩求解器进行少量步骤的常微分方程采样，在保持推理成本可控的同时丰富细节。 由于在UHD情况下多步生成可能会出现数值不稳定性，我们提出了一种病态抑制方案，通过对特征诱导注意力矩阵施加条件数正则化来改善收敛性和跨尺度一致性。我们的方法在4K（3840x2160）或更高分辨率的模糊图像上表现出色。
URL
https://arxiv.org/abs/2603.10517
