Lensless imaging reconstructs scenes from highly multiplexed measurements, resulting in a severely ill-posed inverse problem. In this work, we identify a fundamental trade-off between measurement consistency, perceptual quality, and inference speed across lensless reconstruction paradigms. Traditional methods favor consistency but produce perceptually degraded results, supervised approaches achieve high-quality reconstructions with fast inference but may violate physical constraints, and diffusion-prior methods achieve high perceptual quality and consistency--particularly when structured constraints such as range-null decomposition are used--but remain slow due to iterative sampling. Motivated by this observation, we propose Null-Space Diffusion Distillation (NSDD), a single-pass reconstruction model that distills structured diffusion-prior inference into an efficient feed-forward network. NSDD learns to produce high-quality reconstructions that preserve measurement consistency while avoiding costly iterative sampling. Experimental results demonstrate that NSDD achieves perceptual quality and consistency competitive with diffusion-prior methods, while providing significantly faster inference and offering a favorable balance across all three objectives. Furthermore, ablation experiments show that distilling the range--null decomposition improves reconstruction quality and robustness over unstructured full-reconstruction distillation, including on unseen real scenes. These results highlight the potential of structure-aware distillation for efficient lensless imaging. Code is available at github.com/JRCSAVSN/NullSpaceDiffusionDistillation.

翻译：无透镜成像从高度复用测量中重构场景，这导致了一个严重病态的逆问题。在本工作中，我们识别出无透镜重构范式在测量一致性、感知质量与推理速度之间存在根本性的权衡。传统方法倾向于一致性但产生感知退化的结果，监督学习方法虽实现高质量重构与快速推理但可能违反物理约束，而扩散先验方法——尤其在采用如范围-零分解等结构化约束时——能达到高感知质量与一致性，但因迭代采样而速度缓慢。基于此观察，我们提出零空间扩散蒸馏（NSDD），一种单次通过的重构模型，将结构化扩散先验推理蒸馏为高效的前馈网络。NSDD学习生成保持测量一致性的高质量重构，同时避免昂贵的迭代采样。实验结果表明，NSDD在感知质量与一致性上可与扩散先验方法相媲美，同时提供显著更快的推理速度，并在所有三个目标间实现有利平衡。此外，消融实验显示，蒸馏范围-零分解相较于非结构化全重构蒸馏，能提升重构质量与鲁棒性，包括在未见真实场景上的表现。这些结果凸显了结构感知蒸馏在高效无透镜成像中的潜力。代码见 github.com/JRCSAVSN/NullSpaceDiffusionDistillation。