深度学习下的散射成像:物理与数据联合建模优化（特邀）

为了利用被散射的光信号实现成像，越来越多的散射成像方法被提出。其中深度学习以其强大的数据表征和信息提取能力在散射成像领域发挥着重要的作用。相较于传统散射成像方法，基于深度学习的散射成像方法在成像速度、质量、信息维度等方面都有着巨大的优势。但是，模型训练、模型泛化等问题也制约着该方法的发展。因此，越来越多的研究将物理过程与基于数据驱动的方法进行联合建模，利用物理先验指导神经网络优化。相较于单纯的数据驱动方法而言，物理-数据联合建模的方法对数据量、神经网络参数量的依赖程度大大降低，在保证成像质量的前提下有效降低数据获取难度及对实验环境的要求。联合建模优化的方式实现了介质、目标类型等散射成像中关键节点的泛化。同时在训练过程方面，实现了从有监督到半监督再到无监督的训练优化过程迭代，不同模型和监督方式的提出大大提升了基于深度学习方法的训练效率，在降低对硬件和时间成本的同时，提升了基于深度学习的散射成像方法在非实验室场景应用的可能性。

Scattering imaging with deep learning: Physical and data joint modeling optimization (invited)

More scattering imaging methods have been proposed to realize imaging using scattered optical signals. Deep learning plays an important role in the field of imaging through scattering medium with its powerful data representation ability and information extraction ability. Compared with traditional scattering imaging methods, deep learning-based scattering imaging methods have great advantages in imaging speed, imaging quality, information dimension, and other aspects. However, the problems of model training, model generalization also restrict the development of this method. Therefore, more and more studies jointly model physical processes with data-driven-based methods and use physical priors to guide neural network optimization. Compared with the simple data-driven method, the physical-data joint modeling method greatly reduces the dependence on the amount of data and the number of neural network parameters, which can effectively reduce the difficulty of data acquisition and the requirements for experimental environment under the premise of ensuring the imaging quality. The joint modeling optimization method realizes the generalization of the medium and the type of hidden targets. At the same time, the training strategy of those methods is also being optimized which is realized from the supervised to semi-supervised and then to unsupervised. The proposed different models and supervision strategies greatly improve training efficiency. Those advantages improve the method of imaging through scattering medium based on the deep learning scenario application possibility out of the laboratory while reducing the cost of hardware and time.