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.