Imaging through scattering media is a fundamental and pervasive challenge in fields ranging from medical diagnostics to astronomy. A promising strategy to overcome this challenge is wavefront modulation, which induces measurement diversity during image acquisition. Despite its importance, designing optimal wavefront modulations to image through scattering remains under-explored. This paper introduces a novel learning-based framework to address the gap. Our approach jointly optimizes wavefront modulations and a computationally lightweight feedforward "proxy" reconstruction network. This network is trained to recover scenes obscured by scattering, using measurements that are modified by these modulations. The learned modulations produced by our framework generalize effectively to unseen scattering scenarios and exhibit remarkable versatility. During deployment, the learned modulations can be decoupled from the proxy network to augment other more computationally expensive restoration algorithms. Through extensive experiments, we demonstrate our approach significantly advances the state of the art in imaging through scattering media. Our project webpage is at https://wavemo-2024.github.io/.

翻译：透过散射介质成像是从医学诊断到天文学等领域中一项基础且普遍的挑战。克服这一挑战的有效策略是波前调制，它能在图像采集过程中引入测量多样性。尽管其重要性不言而喻，但设计用于透过散射成像的最优波前调制仍未被充分探索。本文提出了一种新颖的基于学习的框架来填补这一空白。我们的方法联合优化了波前调制和一个计算轻量的前馈“代理”重建网络。该网络经过训练，能够利用这些调制所修改的测量数据，恢复被散射遮蔽的场景。我们的框架生成的习得调制能有效推广到未见过的散射场景，并展现出卓越的通用性。在部署时，习得调制可与代理网络解耦，以增强其他计算代价更高的复原算法。通过大量实验，我们证明了该方法显著推动了透过散射介质成像领域的技术水平。我们的项目网页位于 https://wavemo-2024.github.io/。