A single metasurface operates as a reusable generator of different keys to encode various secret images in a computational way.

The multiplexing and de-multiplexing of orbital angular momentum (OAM) beams are critical issues in optical communication. Optical diffractive neural networks have been introduced to perform sorting, generation, multiplexing, and de-multiplexing of OAM beams. However, conventional diffractive neural networks cannot handle OAM modes with a varying spatial distribution of polarization directions. Herein, we propose a polarized optical deep diffractive neural network that is designed based on the concept of dielectric rectangular micro-structure meta-material. Our proposed polarized optical diffractive neural network is optimized to sort, generate, multiplex, and de-multiplex polarized OAM beams. The simulation results show that our network framework can successfully sort 14 kinds of orthogonally polarized vortex beams and de-multiplex the hybrid OAM beams into Gauss beams at two, three, and four spatial positions, respectively. Six polarized OAM beams with identical total intensity and eight cylinder vector beams with different topology charges have also been sorted effectively. Additionally, results reveal that the network can generate hybrid OAM beams with high quality and multiplex two polarized linear beams into eight kinds of cylinder vector beams.

In previous single-pixel imaging systems, the light source was generally idle with respect to time. Here, we propose a novel image fusion and visible watermarking scheme based on Fourier single-pixel imaging (FSPI) with a multiplexed time-varying (TV) signal, which is generated by the watermark pattern hidden in the light source. We call this scheme TV-FSPI. With TV-FSPI, we can realize high-quality visible image watermarking, encrypted image watermarking and full-color visible image watermarking. We also discuss the extension to invisible watermarking based on TV-FSPI. Furthermore, we don’t have to recode illumination patterns, because TV-FSPI can be extended to existing mainstream illumination patterns, such as random illumination mode and Hadamard illumination mode. Thus TV-FSPI has the potential to be used in single-pixel broadcasting system and multi-spectral single-pixel imaging system.

How can a target’s differential image of multiple components at a certain degree of freedom (DOF) of light (e.g., wavelength, polarization, and position) be obtained? Previous schemes often need two steps, i.e., first collecting each component of the target simultaneously with multiple detectors or sequentially with a single detector, then performing the difference operation in the digital postprocessing. Based on the principle of computational ghost imaging (GI), we here take advantage of the natural complementarity in space to design an illumination mode, which is no longer the spatiotemporal fluctuation of photon number (light intensity) but the photons with different components under the same DOF. By applying this engineered illumination to computational GI, termed ghost difference imaging (GDI), we can achieve differential imaging of multiple components of a certain DOF in a single-round acquisition using only one single-pixel detector with no extra digital-subtraction operations but higher SNR. Both multiwavelength-difference GDI and position-difference GDI are well demonstrated in simulations and experiments. Further, the GDI scheme reduces the number of sampling times for differential imaging while also increasing photons’ utilization. Our work, therefore, provides an insight for GI modality, along with a straightforward and low-cost update of the light path, which is suitable for all computational GI systems using the digital micromirror device.

The principle of computational ghost imaging (GI) offers a potential application in optical encryption. Nevertheless, large numbers of keys composed of random or specific patterns set an obstacle to its application. Here, we propose a series of pattern compression methods based on computational GI, in which thousands of patterns are replaced by a single standard image (i.e., two-dimensional data), a sequence of numbers (i.e., one-dimensional data) or the fractional part of an irrational number (i.e., zero-dimensional data). Different pattern compression methods are tested in both simulations and experiments, and their error tolerances in encryption are further discussed. Our proposed methods can greatly reduce the pattern amount and enhance encryption security, which pushes forward the application of computational GI, especially in optical encryption.