Prior-free imaging unknown target through unknown scattering medium

Abstract

Imaging through scattering medium based on deep learning has been extensively studied. However, existing methods mainly utilize paired data-prior and lack physical-process fusion, and it is difficult to reconstruct hidden targets without the trained networks. This paper proposes an unsupervised neural network that integrates the universal physical process. The reconstruction process of the network is irrelevant to the system and only requires one frame speckle pattern and unpaired targets. The proposed network enables online optimization by using physical process instead of fitting data. Thus, large-scale paired data no longer need to be obtained to train the network in advance, and the proposed method does not need prior information. The optimization of the network is a physical-based process rather than a data mapping process, and the proposed method also increases the insufficient generalization ability of the learning-based method in scattering medium and targets. The universal applicability of the proposed method to different optical systems increases the likelihood that the method will be used in practice.

Data‐Class‐Specific All‐Optical Transformations and Encryption

“…Furthermore, some of the emerging digital imaging techniques that are optimized to see through random scattering media are unlikely to compromise our data‐class encryption scheme enabled by diffractive networks due to their requirement of prior knowledge of the scattering medium properties [ 38–41 ] or the input‐output measurement pairs. [ 42–45 ] In addition to these, due to the absorbing/blocking areas of a D 2 NN, its architecture does not present time‐reversal symmetry; [ 21 ] when this feature is combined with the fact that the optical phase information is lost at the image sensor‐array, it becomes unattainable for adversaries to decipher the original input objects through backward field propagation operations applied on the acquired intensity‐only images, further reinforcing our system's security.…”

Steady state non-line of sight imaging via unsupervised network

“…[1][2][3] Intensity-based methods have been widely studied because they are simple, cheap and can achieve single frame imaging. [4][5][6] Many researchers have studied steady state NLOS imaging at different views to solve different problems, such as imaging or locating hidden targets. However, due to the strong aliasing degree of the scattered signals, there is almost no structured information in speckle produced by relay wall, which leads to the low quality of the target reconstructed by the traditional method.…”