Single-pixel imaging (SPI) can capture 2D images of the target with only a nonpixelated detector, showing promising application potential in nonvisible spectral imaging, low-photon imaging, lidar, and other extreme imaging fields. However, the imaging mechanism of traditional SPI makes it difficult to achieve high imaging speed, which is a primary barrier for its widespread application. To address this issue, in this work, we propose and demonstrate a novel high-speed 2D and 3D imaging scheme based on traditional SPI, termed time-resolved single-pixel imaging (TRSPI). Previous SPI works mainly utilize correlation between a stable target and iterative illumination masks to reconstruct a single image. In TRSPI, by further exploiting correlation information between a dynamic scene and every static mask, we can reconstruct a series of time-varying images of the dynamic scene, given the dynamic scene is repetitive or reproducible. Experimentally, we conducted 2D and 3D imaging on a rotating chopper with a speed of 4800 revolutions per minute (rpm), and imaging speeds up to 2,000,000 fps. It is believed that this technology not only opens up a novel application direction for SPI, but also will provide a powerful solution for high-speed imaging.

Hilbert transform (HT) has been employed to compensate phase error arising from the nonlinear effect in phase shifting profilometry (PSP). However, in most common situations, pure HT may lead to a significant system error, which has a negative impact on subsequent phase error compensation. In this paper, system error from HT of non-stationary and non-continuous fringe is analyzed, and then a novel phase error suppression approach is presented. The cosine fringe without direct current (DC) component is reconstructed to eliminate the influence of non-smooth reflectivity, and the fractional periods at both ends of the reconstructed fringe are extended to generate fringe with integer number of periods. And then the HT is applied to the reconstructed and extended fringe. Finally, a revised phase-shifting algorithm is employed to calculate the phase with the fringe after HT. The proposed approach is suitable for PSP of the surface with non-smooth reflectivity (e.g. texture of complex colors), which is demonstrated in a series of experiments.

Tracking and imaging for high-speed moving objects have a wide range of application prospects in many fields, such as transportation and security monitoring. In this paper, the chrome plated masks are designed to carry geometric moment and random binary encoding patterns, combined with single pixel detectors, to achieve real-time tracking and imaging of fast-moving object. By using the geometric moment principle to obtain the motion trajectory of the object, coding sub-patterns and corresponding detection signals are extracted at different positions to reconstruct the image of the object. Multiple optical paths are established to avoid the side effects of motion error, and a dedicated calibration approach is proposed to improve the accuracy of tracking. The feasibility of the method is demonstrated by simulations and experiments. The proposed scheme, which modulates light with static mask instead of spatial light modulator (SLM), improves the speed and spectral range meanwhile reduces the system cost.

When implementing the phase shifting profilometry to reconstruct an object, the object is always required to be kept stable as multiple fringe patterns are required. Movement during the measurement will cause failed reconstruction. This paper proposes a general model describing the fringe patterns with any three-dimensional movement based on phase shifting profilometry. The object movement is classified as five types and their characteristics are analyzed respectively. Then, by introducing a virtual plane, the influence on the phase value caused by different types of movement is described mathematically and a new model including movement information is proposed. At last, with the help of the movement tracking and least-square algorithm, the moving object is reconstructed with high accuracy. The proposed method can remove the reference plane during the reconstruction of the moving object, which extends the application range of the phase shifting profilometry. The effectiveness of the proposed model is verified by the experiments.

A neural network based known-plaintext cryptanalysis for a computational-ghost-imaging (CGI) cryptosystem is proposed, which utilizes relevant physical priors as references and conditions during training. It retrieves more significant qualified and higher accurate attacking results with much more complicated human face dataset that fairly improves previous learning based works. Popularly employed neutral networks such as the convolutional neural network (CNN), recurrent neural network (RNN) and U-Net are further compared. However, our proposed method defeats them with the best attacking results, which is also proved by the following detailed quantitative analysis. On the other hand, compared with traditional methods utilizing phase recovering to estimate the privacy key, the proposed network method develops an end-to-end way that directly converts the ciphertext to the corresponding plaintext. The method is capable of high-volume attacking mission with rather highly qualified attacking results and fast response after valid training. Both computer simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed method.