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Terahertz coded‐aperture imaging technology (TCAI) has attracted considerable attention due to its high‐resolution characteristics in the field of staring imaging. Recent research in metasurface have demonstrated its potential for enhancing terahertz applications by enabling precise control over electromagnetic waves. However, traditional methods for image reconstruction often depend on theoretical calculation data, which are susceptible to environmental noise and model inaccuracies. To solve this problem, this article proposes an unsupervised learning method that iteratively corrects these errors through periodic training. This method models the physical process as a constraint in the training work, starting with theoretical calculations of the radiation field matrix to predict scattering coefficients, which reduces data requirements and enhances robustness. The simulation results demonstrate its superior image quality and noise resistance compared to traditional algorithms, especially under low sampling rates, which provides a strong foundation for practical implementations of TCAI. A physics‐constrained unsupervised learning framework with periodic error correction is proposed for terahertz coded‐aperture imaging. The method improves reconstruction robustness against noise and model mismatch, while reducing reliance on large‐scale labeled data. It enhances generalization and interpretability, offering a practical solution for accurate image recovery in terahertz imaging systems under limited sampling and real‐world conditions.

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.

Coded aperture imaging (CAI) is a well-established computational imaging technique consisting of two steps, namely the optical recording of an object using a coded mask, followed by a computational reconstruction using a computational algorithm using a pre-recorded point spread function (PSF). In this tutorial, we introduce a simple yet elegant technique called spatial ensemble mapping (SEM) for CAI that allows us to tune the axial resolution post-recording from a single camera shot recorded using an image sensor. The theory, simulation studies, and proof-of-concept experimental studies of SEM-CAI are presented. We believe that the developed approach will benefit microscopy, holography, and smartphone imaging systems.

Coded aperture imaging (CAI) is a powerful imaging technology that has rapidly developed during the past decade. CAI technology and its integration with incoherent holography have led to the development of several cutting-edge imaging tools, devices, and techniques with widespread interdisciplinary applications, such as in astronomy, biomedical sciences, and computational imaging. In this review, we provide a comprehensive overview of the recently developed CAI techniques in the framework of incoherent digital holography. The review starts with an overview of the milestones in modern CAI technology, such as interferenceless coded aperture correlation holography, followed by a detailed survey of recently developed CAI techniques and system designs in subsequent sections. Each section provides a general description, principles, potential applications, and associated challenges. We believe that this review will act as a reference point for further advancements in CAI technologies.

Metamaterial-based coded aperture imaging (MCAI) is a forward-looking radar imaging technique based on wavefront modulation. The scattering coefficients of the target can resolve as an ill-posed inverse problem. Data-based deep-learning methods provide an efficient, but expensive, way for target reconstruction. To address the difficulty in collecting paired training data, an untrained deep radar-echo-prior-based MCAI (DMCAI) optimization model is proposed. DMCAI combines the MCAI model with a modified U-Net for predicting radar echo. A joint loss function based on deep-radar echo prior and total variation is utilized to optimize network weights through back-propagation. A target reconstruction strategy by alternatively using the imaginary and real part of the radar echo signal (STAIR) is proposed to solve the DMCAI. It makes the target reconstruction task turn into an estimation from an input image by the U-Net. Then, the optimized weights serve as a parametrization that bridges the input image and the target. The simulation and experimental results demonstrate the effectiveness of the proposed approach under different SNRs or compression measurements.

Image enhancement techniques (such as edge and contrast enhancement) are essential for many imaging applications. In incoherent holography techniques such as Fresnel incoherent correlation holography (FINCH), the light from an object is split into two, each of which is modulated differently from one another by two different quadratic phase functions and coherently interfered to generate the hologram. The hologram can be reconstructed via a numerical backpropagation. The edge enhancement procedure in FINCH requires the modulation of one of the beams by a spiral phase element and, upon reconstruction, edge-enhanced images are obtained. An optical technique for edge enhancement in coded aperture imaging (CAI) techniques that does not involve two-beam interference has not been established yet. In this study, we propose and demonstrate an iterative algorithm that can yield from the experimentally recorded point spread function (PSF), a synthetic PSF that can generate edge-enhanced reconstructions when processed with the object hologram. The edge-enhanced reconstructions are subtracted from the original reconstructions to obtain contrast enhancement. The technique has been demonstrated on FINCH and CAI methods with different spectral conditions.

We present Inverse Airborne Optical Sectioning (IAOS), an optical analogy to Inverse Synthetic Aperture Radar (ISAR). Moving targets, such as walking people, that are heavily occluded by vegetation can be made visible and tracked with a stationary optical sensor (e.g., a hovering camera drone above forest). We introduce the principles of IAOS (i.e., inverse synthetic aperture imaging), explain how the signal of occluders can be further suppressed by filtering the Radon transform of the image integral, and present how targets’ motion parameters can be estimated manually and automatically. Finally, we show that while tracking occluded targets in conventional aerial images is infeasible, it becomes efficiently possible in integral images that result from IAOS.

We present a new set of near orthogonal codes which we call Hadamard Aperiodic Interval (HAPI) codes and demonstrate their utility for parallel multi-transmitter synthetic aperture imaging. The codes are tri-state and sparse. Locations of non-zero bits are based on marks in a sequence of aperiodic intervals, also known as a Golomb ruler. The values of non-zero bits are selected from Hadamard sequences that are mutually orthogonal. This ensures that cross-correlation sidelobe magnitudes between differing codes are bounded by unity while the autocorrelation approaches a delta function with mainlobe-to-sidelobe levels scaling with the number of non-zero bits. We use simulations to demonstrate the potential of the codes for synthetic aperture imaging. A multiplicity of transmitter elements is used to transmit codes simultaneously, with a different code for each element. Echo signals are received from a multiplicity of transducer elements in parallel. Channel data from each receiver element are cross-correlated with respective HAPI codes to estimate the transmit–receive signature associated with each transmitter–receiver pair while minimizing crosstalk. This estimate of the full transmit–receive synthetic aperture dataset is then used to form high-quality images demonstrating image quality and signal-to-noise ratio improvements over multiple flash angle imaging and synthetic aperture imaging methods for linear arrays. We also demonstrate simulated full volume synthetic aperture imaging with random sparse arrays, possible with one extended HAPI code-set transmit event.

As a promising terahertz radar imaging technology, phaseless terahertz coded-aperture imaging (PL-TCAI) has many advantages such as simple system structure, forward-looking imaging and staring imaging and so forth. However, it is very difficult to recover a target only from its intensity measurements. Although some methods have been proposed to deal with this problem, they require a large number of intensity measurements for both sparse and extended target reconstruction. In this work, we propose a method for PL-TCAI by modeling target scattering coefficient as being in the range of a generative model. Theoretically, we analyze and model the system structure, derive the matrix imaging equation, and then study the deep phase retrieval algorithm. Numerical tests based on different generative models show that the targets with the different spareness can achieve high resolution reconstruction when the number of intensity measurements are smaller than the number of target grids. Also, we find that the proposed method has good anti-noise and stability.

In this paper, the imaging quality of a three-aperture Golay3 imaging system is studied. The formula of point spread function (PSF) is derived when piston errors exist among each subaperture. The 1951 resolution target is used to analyze the imaging quality under the influence of different piston errors. To evaluate the imaging quality quantificationally, the Tenengrad function based on image gradient definition is employed. The fast steering mirror (FSM) controlled by PZT is used to eliminate the phase error in the optical structure of the Golay3 imaging system. The principle and formula of piston error correction are introduced. Using the Tenengrad function, the relationship between the movement of PZT and the image sharpness is obtained. Through the simulation of piston error correction, the piston error p = λ /2 in the system can be completely corrected when the PZT movement p’ = 0.35 λ .