A kind of compact all-optical learning-based neural network has been constructed and characterized for efficiently performing a robust layered diffractive shaping of laser beams. The data-driven control lightwave strategy demonstrates some particular advantages such as smart or intelligent light beam manipulation, optical data statistical inference and incident beam generalization. Based on the proposed method, several typical aberrated light fields can be effectively modulated into the desired fashion including the featured flat-top beams, an arrayed sub-beam arrangement and complex annular fringes compared with conventional GS-based DOEs. An actual THz laser is utilized to evaluate the effectiveness of the method developed.

Light-field imaging is a crucial and straightforward way of measuring and analyzing surrounding light worlds. In this paper, a dual-polarized light-field imaging micro-system based on a twisted nematic liquid-crystal microlens array (TN-LCMLA) for direct three-dimensional (3D) observation is fabricated and demonstrated. The prototyped camera has been constructed by integrating a TN-LCMLA with a common CMOS sensor array. By switching the working state of the TN-LCMLA, two orthogonally polarized light-field images can be remapped through the functioned imaging sensors. The imaging micro-system in conjunction with the electric-optical microstructure can be used to perform polarization and light-field imaging, simultaneously. Compared with conventional plenoptic cameras using liquid-crystal microlens array, the polarization-independent light-field images with a high image quality can be obtained in the arbitrary polarization state selected. We experimentally demonstrate characters including a relatively wide operation range in the manipulation of incident beams and the multiple imaging modes, such as conventional two-dimensional imaging, light-field imaging, and polarization imaging. Considering the obvious features of the TN-LCMLA, such as very low power consumption, providing multiple imaging modes mentioned, simple and low-cost manufacturing, the imaging micro-system integrated with this kind of liquid-crystal microstructure driven electrically presents the potential capability of directly observing a 3D object in typical scattering media.

Plenoptic cameras have received a wide range of research interest because it can record the 4D plenoptic function or radiance including the radiation power and ray direction. One of its important applications is digital refocusing, which can obtain 2D images focused at different depths. To achieve digital refocusing in a wide range, a large depth of field (DOF) is needed, but there are fundamental optical limitations to this. In this paper, we proposed a plenoptic camera with an extended DOF by integrating a main lens, a tunable multi-focus liquid-crystal microlens array (TMF-LCMLA), and a complementary metal oxide semiconductor (CMOS) sensor together. The TMF-LCMLA was fabricated by traditional photolithography and standard microelectronic techniques, and its optical characteristics including interference patterns, focal lengths, and point spread functions (PSFs) were experimentally analyzed. Experiments demonstrated that the proposed plenoptic camera has a wider range of digital refocusing compared to the plenoptic camera based on a conventional liquid-crystal microlens array (LCMLA) with only one corresponding focal length at a certain voltage, which is equivalent to the extension of DOF. In addition, it also has a 2D/3D switchable function, which is not available with conventional plenoptic cameras.

We propose a broad-spectrum diffractive deep neural network (BS-D²NN) framework, which incorporates multiwavelength channels of input lightfields and performs a parallel phase-only modulation using a layered passive mask architecture. A complementary multichannel base learner cluster is formed in a homogeneous ensemble framework based on the diffractive dispersion during lightwave modulation. In addition, both the optical sum operation and the hybrid (optical–electronic) maxout operation are performed for motivating the BS-D²NN to learn and construct a mapping between input lightfields and truth labels under heterochromatic ambient lighting. The BS-D²NN can be trained using deep learning algorithms to perform a kind of wavelength-insensitive high-accuracy object classification.

As an optical-based classifier of the physical neural network, the independent diffractive deep neural network ( D 2 N N ) can be utilized to learn the single-view spatial featured mapping between the input lightfields and the truth labels by preprocessing a large number of training samples. However, it is still not enough to approach or even reach a satisfactory classification accuracy on three-dimensional (3D) targets owing to already losing lots of effective lightfield information on other view fields. This Letter presents a multiple-view D 2 N N s array (MDA) scheme that provides a significant inference improvement compared with individual D 2 N N or Res- D 2 N N by constructing a different complementary mechanism and then merging all base learners of distinct views on an electronic computer. Furthermore, a robust multiple-view D 2 N N s array (r-MDA) framework is demonstrated to resist the redundant spatial features of invalid lightfields due to severe optical disturbances.

To develop an intelligent imaging detector array, a diffractive neural network with strong robustness based on the Weight-Noise-Injection training is proposed. According to layered diffractive transformation under existing several errors, an accurate and fast object classification can be achieved. The fact that the mapping between the input image and the label in Weight-Noise-Injection training mode can be learned, means that the prediction of the optical network being insensitive to disturbances so as to improve its noise resistance remarkably. By comparing the accuracy under different noise conditions, it is verified that the proposed model can exhibit a higher accuracy.