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This paper proposes a 4 × 4 beamforming network (BFN) using a novel configuration. The proposed architecture utilizes a two‐layer multi‐aperture and multislot coupler (MA/MSC). Power coupling between ports within each layer is achieved through designed apertures. Additionally, power distribution between the upper and lower layers is facilitated by the embedded slots between the two layers. To validate the performance of the proposed design, a four‐beam antenna is fabricated by connecting a 4 × 2 SIW slot antenna to the BFN. The satisfactory performance of the proposed BFN is validated through antenna measurements. Compared with conventional designs, the proposed BFN offers several advantages: fewer components, elimination of crossovers, and compact size (approximately 20 mm × 38 mm). These features make it highly suitable for fifth‐generation (5G) wireless communication applications due to its size and simplicity.

Multispectral imaging (MSI) enables the acquisition of spatial and spectral image-based information in one process. Spectral scene information can be used to determine the characteristics of materials based on reflection or absorption and thus their material compositions. This work focuses on so-called multi aperture imaging, which enables a simultaneous capture (snapshot) of spectrally selective and spatially resolved scene information. There are some limiting factors for the spectral resolution when implementing this imaging principle, e.g., usable sensor resolutions and area, and required spatial scene resolution or optical complexity. Careful analysis is therefore needed for the specification of the multispectral system properties and its realisation. In this work we present a systematic approach for the application-related implementation of this kind of MSI. We focus on spectral system modeling, data analysis, and machine learning to build a universally usable multispectral loop to find the best sensor configuration. The approach presented is demonstrated and tested on the classification of waste, a typical application for multispectral imaging.

Knowledge of the full geometry of abdominal aortic aneurysms (AAA) and local, mechanical wall parameters using ultrasound (US) can contribute to a better assessment of the AAA’s mechanical state, prediction of growth and possible risk of rupture. Such an assessment is currently limited by the anisotropic lumen-wall contrast and the resolution of conventional US. The recent introduction of ultrafast dual-aperture imaging enhances image quality, using two transducers that alternately transmit and receive simultaneously (“bistatic” US). In this study, dual-aperture, bistatic US imaging is assessed in 43 AAA patients. Results were compared to single-aperture ultrafast imaging. Bistatic imaging was demonstrated successfully in 40 patients. Compared to single-aperture imaging, the median wall-lumen generalized contrast-to-noise ratio (gCNR) was significantly increased by 0.13 (+27%). By compounding axial displacements from multiple directions, we show the feasibility of local strain quantification: not only in the vessel wall but also in low contrast regions, such as the intraluminal thrombus (ILT). Multi-aperture ultrasound imaging can provide the clinician with high quality imaging, which contributes towards a better understanding of AAA development and patient-specific rupture risk analysis, by allowing the accurate assessment of tissue properties and function.

A probabilistic method to remove the random telegraph signal (RTS) noise and to increase the signal level is proposed, and was verified by simulation based on measured real sensor noise. Although semi-photon-counting-level (SPCL) ultra-low noise complementary-metal-oxide-semiconductor (CMOS) image sensors (CISs) with high conversion gain pixels have emerged, they still suffer from huge RTS noise, which is inherent to the CISs. The proposed method utilizes a multi-aperture (MA) camera that is composed of multiple sets of an SPCL CIS and a moderately fast and compact imaging lens to emulate a very fast single lens. Due to the redundancy of the MA camera, the RTS noise is removed by the maximum likelihood estimation where noise characteristics are modeled by the probability density distribution. In the proposed method, the photon shot noise is also relatively reduced because of the averaging effect, where the pixel values of all the multiple apertures are considered. An extremely low-light condition that the maximum number of electrons per aperture was the only 2 e − was simulated. PSNRs of a test image for simple averaging, selective averaging (our previous method), and the proposed method were 11.92 dB, 11.61 dB, and 13.14 dB, respectively. The selective averaging, which can remove RTS noise, was worse than the simple averaging because it ignores the pixels with RTS noise and photon shot noise was less improved. The simulation results showed that the proposed method provided the best noise reduction performance.

Monocular 3D exploration of underwater objects is a growing field of application due to powerful new developments of computer technology, hardware components, and algorithms such as AI-supported modules. Using appropriate software tools, video streams of simple cameras may produce complete 3D models. This technique makes it straightforward and convenient to collect image data and produce 3D models of certain objects. In this work we introduce several approaches for further simplification of underwater camera systems for production of 3D models of underwater structures. The principles of monocular 3D reconstruction using image sequences and those of multi-aperture camera technology are described and their use for underwater applications are discussed. The initial experimental results are presented, along with an outlook to approaches of small and lightweight powerful 3D reconstruction systems for (not exclusively) diver use.

Conventional multispectral imaging systems based on bandpass filters struggle to record multispectral videos with high spatial resolutions because of their limited light efficiencies. This paper proposes a multi-aperture multispectral imaging system based on notch filters that overcomes this limitation by allowing light from most of the spectrum to pass through. Based on this imaging principle, a prototype multi-aperture multispectral imaging system comprising notch filters was built and demonstrated. Further, a dictionary learning- and total variation-based spectral super-resolution algorithm was developed to reconstruct spectral images. The simulation results obtained using public multispectral datasets showed that, compared to the dictionary learning-based spectral super-resolution algorithm, the proposed algorithm reconstructed the spectral information with a higher accuracy and removed noise, and the verification experiments confirmed the performance efficiency of the prototype system. The experimental results showed that the proposed imaging system can capture images with high spatial and spectral resolutions under low illumination conditions. The proposed algorithm improved the spectral resolution of the acquired data from 9 to 31 bands, and the average peak signal-to-noise ratio remained above 43 dB, which is 13 dB higher than those of the state-of-the-art coded aperture snapshot spectral imaging methods. Simultaneously, the frame rate of the imaging system was up to 5000 frames/s under natural daylight.

The Adaptive Fiber Coupler (AFC) array is an innovative device designed to achieve the stable and efficient coupling of free-space light into optical fibers. To mitigate the effects of atmospheric turbulence, the Stochastic Parallel Gradient Descent (SPGD) algorithm has been predominantly adopted as the control method for AFC systems. However, due to the dynamic nature of atmospheric turbulence, the relatively slow convergence speed of the SPGD algorithm poses significant challenges for practical applications. This paper presents a model-based AFC control system that effectively mitigates wavefront aberrations caused by atmospheric turbulence. The performance of this system was evaluated in comparison with the SPGD algorithm under different turbulence levels and different sub-aperture numbers. Results show that the model-based AFC system converges faster than the SPGD-based AFC system under identical conditions. Additionally, the number of iterations required by the model-based AFC system remains relatively stable, whereas the SPGD-based AFC system demonstrates substantial variability depending on the number of sub-apertures and turbulence levels. As the turbulence level increases, the SPGD-based AFC system requires a greater number of iterations to achieve convergence. The proposed model-based method offers a robust and efficient solution for adaptive multi-aperture fiber coupling systems, which provides theoretical and technical support for the practical application of AFC array.

To address the issues of insufficient detector target size and high system complexity in infrared bionic compound-eye systems, this paper designs a miniaturized cooled medium-wave infrared curved bionic compound-eye optical system specifically for large target surface detectors and develops a proof-of-concept prototype for verification. The system comprises three components: (1) a curved multi-aperture array, which consists of 61 sub-apertures with an entrance pupil diameter of 5 mm and a focal length of 10 mm; (2) a cooled planar detector; and (3) a relay imaging system, which adopts secondary imaging technology and achieves the matching between the array and detector with only six infrared lenses. The fill factor is introduced to analyze light energy utilization efficiency, providing a theoretical basis for improving the system’s signal-to-noise ratio and spatial information collection capability; meanwhile, the focal length distribution and pupil matching are analyzed to ensure the system’s optical performance. The system operates within the 3.7–4.8 μm wavelength band, with a total focal length of 3.08 mm, F-number of 2, and field of view reaching 108°. Simulations demonstrate that all sub-aperture imaging channels have MTF values greater than 0.47 at 33.3 lp/mm, with distortion less than 3%. Imaging test results verify that the system possesses excellent imaging performance.

In the present study, the advantages of multispectral imaging over hyperspectral imaging in real-time spectral imaging are briefly analyzed, and the advantages and disadvantages of snapshot spectral imaging and other spectral imaging technologies are briefly described. The technical characteristics of artificial compound eyes and multi-aperture imaging and the research significance of snapshot artificial compound eye multispectral imaging are also introduced. The classification and working principle of the snapshot artificial compound eye multispectral imaging system are briefly described. According to the realization method of the optical imaging system, the ACE snapshot multi-aperture multispectral imaging system is divided into plane and curved types. In the planar compound eye spectral imaging system, the technical progress of the multispectral imaging system based on the thin observation module by bound optics (TOMBO) architecture and the multispectral imaging system based on the linear variable spectral filter are introduced. At the same time, three curved multispectral imaging systems are introduced. Snapshot artificial compound eye multispectral imaging technology is also briefly analyzed and compared. The research results are helpful to comprehensively understand the research status of snapshot multispectral multi-aperture imaging technology based on artificial compound eyes and to lay the foundation for improving its comprehensive performance even further.

In extremely low-light conditions, random telegraph signal (RTS) noise and dark current white defects become visible. In this paper, a multi-aperture imaging system and selective averaging method which removes the RTS noise and the dark current white defects by minimizing the synthetic sensor noise at every pixel is proposed. In the multi-aperture imaging system, a very small synthetic F-number which is much smaller than 1.0 is achieved by increasing optical gain with multiple lenses. It is verified by simulation that the effective noise normalized by optical gain in the peak of noise histogram is reduced from 1.38e⁻ to 0.48 e⁻ in a 3 × 3-aperture system using low-noise CMOS image sensors based on folding-integration and cyclic column ADCs. In the experiment, a prototype 3 × 3-aperture camera, where each aperture has 200 × 200 pixels and an imaging lens with a focal length of 3.0 mm and F-number of 3.0, is developed. Under a low-light condition, in which the maximum average signal is 11e⁻ per aperture, the RTS and dark current white defects are removed and the peak signal-to-noise ratio (PSNR) of the image is increased by 6.3 dB.