高效学习的透过未知散射介质的相位恢复方法  

Efficient learning-based phase retrieval method through unknown scattering media

作　　者：朱硕 郭恩来 柏连发 韩静 Zhu Shuo;Guo Enlai;Bai Lianfa;Han Jing(Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学江苏省光谱成像与智能感知重点实验室,江苏南京210094

出　　处：《红外与激光工程》2022年第2期408-416,共9页Infrared and Laser Engineering

基　　金：国家自然科学基金(62031018,61971227,62101255);中国博士后科学基金(2021 M701721)。

摘　　要：透过散射介质对目标进行准确的重建仍然是阻碍人们对深层生物组织成像分析和深空天文观测的主要挑战之一。基于深度学习的散射计算成像方法虽然在成像质量和效率等方面取得了很大的进展,但是针对实际系统中散射介质状态不固定,目标结构具有较高复杂度以及可获取的训练散射数据有限的情况下,单纯利用数据驱动的方法已无法进行准确高效的重建。将散斑相关原理和卷积神经网络强大的数据挖掘和映射能力进行有效的结合,进一步挖掘和利用散斑所包含的冗余信息,实现了仅利用一块薄散射介质对应的散斑数据即可实现透过具有不同统计特性散射介质的复杂目标重构。该方法针对实际散射场景复杂多变和训练样本数据有限的情况,实现了对复杂目标的高质量恢复,有力地推动了基于物理感知的学习方法在实际散射场景中的应用。Imaging through scattering media with high fidelity is still one of the main challenges in imaging analysis of deep biological tissues and distant astronomical observations.The computational imaging method based on deep learning has made significant progress in reconstruction quality and other aspects.However,when the scattering media in the actual system is unstable and the structure of objects is complex,and the obtained scattering dataset for training is limited,the pure data-driven method cannot realize efficient reconstruction.An efficient imaging method was proposed in reconstructing complex objects through unknown thin scattering media with different statistical properties,which was based on the effective combination of the speckle correlation theory and the powerful data mining and mapping capabilities.More information had been unearthed with the redundancy of the speckles and had been fully used with the neural network.This method obtained high-quality recovery of complex objects with complex scattering scenes and the training set is limited.This approach can promote the applications of physics-aware learning in practical scattering scenes.

关 键 词：散射成像 散斑冗余性 功率谱估计 深度学习 相位恢复 

分 类 号：O438[机械工程—光学工程]