Cheng Fei

Short-wave infrared (SWIR) spot detection and localization are essential in optical communication, industrial applications, and laser detection, where illumination and background variations may introduce bright clutter and degrade measurement accuracy. To address this problem, a multidimensional feature discrimination and center-symmetry consistency re-centered weighted-centroid localization method (MFD-CSC-RWC) is proposed to integrate target discrimination and centroid localization within a unified framework. The proposed method achieved a precision of 95.00%, a recall of 92.68%, and an F1 score of 93.83% in the conducted experiment. Additionally, it achieved a two-dimensional (2D) centroid-position repeatability of 0.0078 pixels and a projected adjacent-step displacement root-mean-square error of 0.0440 pixels. The method improves SWIR spot detection and localization in complex environments and supports optical communication and laser-detection applications.

To overcome the limitations of the conventional Von Neumann architecture in energy consumption and processing time, sensing-memory-computing integrated devices inspired by the human visual system demonstrate great potential. Here, a sensing-memory-computing unit based on the hybrid integration of a photodiode and a memristor is proposed, enables flexible hybrid integration of visible or near-infrared photodiodes with digital or analog memristors by a standard semiconductor fabrication process. The storage function is achieved by the large switching ratio of the digital memristor, while the computing function is achieved by the synaptic properties of the analog memristor. A novel self-powered operating scheme based on this device is proposed, in which the photovoltage generated by the photodiode directly drives the memristor, enabling self-powered readout and computation. In image denoising and encoding tasks, the denoising process achieves ultralow training power consumption and self-powered inference, while the encoding process achieves an image classification accuracy of up to 85%. This work promotes the integration of sensing-memory-computing and provides a solid foundation for efficient and biomimetic neuromorphic computing.

Optoelectronic imaging systems are crucial for various sectors, but traditional designs (traditional optoelectronic imaging systems and traditional computational imaging systems) suffer from complexity, limited multidimensional optical information utilization, and a lack of system-level optimization. Metasurfaces offer compact, high-performance optical modulation capabilities, while computational imaging enables the integration of information acquisition and processing. Their intrinsic complementarity has emerged as a prominent research frontier. Existing reviews mainly focus on metasurfaces or computational imaging in isolation, lacking a systems-level perspective. In contrast, this Review focuses on the system-level architectural evolution of optoelectronic imaging systems. On the basis of a systematic analysis of how metasurfaces and computational imaging jointly enable next-generation systems, the new characteristics of next-generation optoelectronic imaging systems via key subsystems are summarized in this review. Meanwhile, we discuss the challenges facing the current applications and future commercialization of next-generation optoelectronic imaging systems.

Laser jamming has emerged as a critical challenge for cameras, with the widespread deployment of automotive light detection and ranging (LiDAR), the risks of detector saturation and image distortion have increased significantly. To address this problem, this paper proposed an anti-laser-jamming imaging strategy based on correlated double sampling (CDS). Through an alternating dual-channel sampling mechanism, the camera can remain operational under laser jamming, ensuring the acquisition of at least one complete image frame. We constructed an experimental system consisting of a laser jamming unit, a photoelectric detection and control unit, a high-speed comparator unit, and a short-wave infrared (SWIR) camera to carry out the verification. Laboratory simulation experiments were conducted under different laser repetition frequencies and pulse widths, followed by anti-laser-jamming experiments using mechanical LiDAR systems and real-vehicle tests in road environments. The results demonstrate that the CDS-based anti-laser-jamming strategy effectively suppresses LiDAR-induced disturbances, achieving stable and high-fidelity imaging performance in road environments. This study provides a practical and scalable solution to enhance the evasion of automotive imaging sensors against laser jamming, offering broad application prospects in autonomous driving, surveillance, and security.

The InGaAs short-wave infrared (SWIR) cameras are now widely used, but there is poor horizontal comparability between cameras. The current test and evaluation of cameras is mainly based on photon transfer theory. The specific test methods are the European Machine Vision Association (EMVA) image sensor and camera characterization test standards and the camera test protocol of teledyne photometrics (TPs). Among them, the conversion gain $K$ [e ${}^{-}$ /digital number (DN)] is a key parameter for uniformly quantifying the performance of SWIR cameras. In practical applications, a single test method is prone to deviations and fails to provide a comprehensive analysis of the error sources. In this article, we compare the theoretical foundations and experimental results of the EMVA and TP testing standards. By plotting the complete noise photon transfer curve (PTC), the mean–variance PTC, and the conversion gain curve, we conduct a comprehensive analysis of the noise and gain characteristics of the SWIR camera. Furthermore, we find that under nonideal conditions and high read noise, the results obtained from the two testing methods can validate each other, thereby determining the true conversion gain of the camera. This work contributes significantly to the in-depth understanding of camera testing standards and provides valuable insights for evaluating the performance of SWIR cameras.

A real-time high dynamic range (HDR) imaging and display method based on correlated double sampling is proposed for short-wave infrared (SWIR) cameras in order to effectively improve its range of brightness and contrast, as well as to obtain more image details. The method utilizes the correlated double sampling technique of the SWIR detector to extend the 14-bit raw image into a 16-bit HDR image and achieve 4 times the HDR imaging. Subsequently, a dynamic range compression process, including logarithmic mapping and histogram equalization, is performed for the 16-bit HDR image to be mapped to an 8-bit display. Finally, the experimental results show that the method can enrich the details of SWIR images under the premise of ensuring real-time imaging.

Optical projection tomography (OPT) is the direct optical equivalent of X-ray computed tomography (CT). To obtain a larger depth of field, traditional OPT usually decreases the numerical aperture (NA) of the objective lens to decrease the resolution of the image. So, there is a trade-off between sample size and resolution. Commercial microfluidic systems can observe a sample in flow mode. In this paper, an OPT instrument is constructed to observe samples. The OPT instrument is combined with commercial microfluidic systems to obtain a three-dimensional and time (3D + T)/four-dimensional (4D) video of the sample. “Focal plane scanning” is also used to increase the images’ depth of field. A series of two-dimensional (2D) images in different focal planes was observed and compared with images simulated using our program. Our work dynamically monitors 3D OPT images. Commercial microfluidic systems simulate blood flow, which has potential application in blood monitoring and intelligent drug delivery platforms. We design an OPT adaptor to perform OPT on a commercial wide-field inverted microscope (Olympusix81). Images in different focal planes are observed and analyzed. Using a commercial microfluidic system, a video is also acquired to record motion pictures of samples at different flow rates. To our knowledge, this is the first time an OPT setup has been combined with a microfluidic system.

In this paper, a real-time, dynamic three-dimensional (3D) shape reconstruction scheme based on the Fourier-transform profilometry (FTP) method is achieved with a short-wave infrared (SWIR) indium gallium arsenide (InGaAs) camera for monitoring applications in low illumination environments. A SWIR 3D shape reconstruction system is built for generating and acquiring the SWIR two-dimensional (2D) fringe pattern of the target. The depth information of the target is reconstructed by employing an improved FTP method, which has the advantages of high reconstruction accuracy and speed. The maximum error in depth for static 3D shape reconstruction is 1.15 mm for a plastic model with a maximum depth of 36 mm. Meanwhile, a real-time 3D shape reconstruction with a frame rate of 25 Hz can be realized by this system, which has great application prospects in real-time dynamic 3D shape reconstruction, such as low illumination monitoring. In addition, for real-time dynamic 3D shape reconstruction, without considering the edge areas, the maximum error in depth among all frames is 1.42 mm for a hemisphere with a depth of 35 mm, and the maximum error of the average of all frames in depth is 0.52 mm.