AI 中文总结
该研究针对基于流的成像逆求解器在固定函数评估次数下的时空信息分配缺陷，提出SAS与MPA组件，可即插即用提升超分辨率等任务的图像恢复质量。
AI 中文摘要
基于流的生成模型已成为无需训练的逆问题求解中强大的图像先验，能够捕捉连贯语义与细粒度结构。尽管具备这些优势，现有基于流的逆求解器主要关注单个更新的设计，在固定函数评估次数（NFEs）下基本忽略了时空信息分配。在时间维度，早期探索不足会使流轨迹陷入错误的语义区域，而将过多NFEs分配给早期阶段则会留给后期细化的预算过少；在空间维度，数据一致性仅在观测区域内提供直接约束，而缺失区域的恢复主要依赖生成先验。为解决这两个问题，我们引入两个互补且无需训练的组件，即频谱自适应调度（SAS）与测量优先注意力（MPA）。对于时间分配，SAS根据退化频谱和logSNR几何将可用NFEs在流时间上进行分配，从而更好地平衡语义探索与细节细化；对于空间传播，MPA利用数据-先验冲突引导信息流向弱约束区域，从而提升语义与结构保真度。在超分辨率、运动去模糊和图像修复等标准图像逆问题上的大量实验表明，所提出的组件可即插即用式集成到现有基于流的逆求解器中，无需重新训练或额外的流模型评估，还能显著提升现有求解器的恢复质量。
英文摘要
Flow-based generative models have emerged as powerful image priors for training-free inverse problem solving, capturing coherent semantics and fine-grained structure. Despite these strengths, existing flow-based inverse solvers primarily focus on the design of individual updates, largely overlooking spatio-temporal information allocation under a fixed number of function evaluations (NFEs). Temporally, insufficient early exploration can trap the flow trajectory in an incorrect semantic basin, whereas excessive allocation of NFEs to early stages leaves little budget for late-stage refinement. Spatially, data consistency provides direct constraints only within observed regions, whereas the recovery of missing regions relies mainly on the generative prior. To address these two issues, we introduce two complementary and training-free components, i.e., Spectrum-Adaptive Scheduling (SAS) and Measurement-Prioritized Attention (MPA). For temporal allocation, SAS distributes the available NFEs over flow time according to the degradation spectrum and logSNR geometry, thus better balancing semantic exploration and detail refinement. For spatial propagation, MPA exploits data-prior conflicts to guide information toward weakly constrained regions, thereby enhancing semantic and structural fidelity. Extensive experiments on standard image inverse problems, e.g., super-resolution, motion deblurring, and inpainting, demonstrate that the proposed components can be integrated into existing flow-based inverse solvers in a plug-and-play manner without retraining or additional flow-model evaluations, and can also significantly improve the restoration quality of existing solvers.
