Abstract
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 this https URL.
Abstract (translated)
通过散射媒体进行成像是一个基本且普遍的挑战，涉及医学诊断、天文学等各个领域。克服这一挑战的一种有前景的方法是波前调制，它在图像采集过程中诱导测量多样性。尽管波前调制非常重要，但为通过散射成像设计最优波前调制仍然是一个未被探索的问题。本文介绍了一种基于学习的新框架来解决这一空白。我们的方法共同优化波前调制和一种计算轻量级的反馈“代理”重构网络。该网络通过这些调制对测量进行训练，以恢复由这些调制导致的场景。我们框架产生的学习调制具有很好的泛化效果，能够有效地扩展到未见过的散射场景，并表现出出色的 versatility。在部署过程中，学习到的调制可以与代理网络分离，从而增强其他更计算密集的恢复算法。通过大量实验，我们证明，我们的方法在散射媒体成像方面显著提高了现有水平。我们的项目网页地址是https://www.example.com。
URL
https://arxiv.org/abs/2404.07985
