Security screening via computational imaging using frequency-diverse metasurface apertures

In the modern tactical imaging environment, new computational imaging (CI) systems and algorithms are being used to improve speed and accuracy for detection tasks. Therefore, a measurement technique is needed to predict the performance of complex non-shift invariant EO/IR imaging systems, including CI systems. Detection performance of traditional imaging systems can be modeled using current system metrics and measurements such as Modulation Transfer Function (MTF), Signal to Noise (SNR), and instantaneous Field of View (iFOV). In this correspondence, we propose a technique to experimentally measure a detection sensitivity metric for non-traditional CI systems. The detection sensitivity metric predicts the upper bound of linear algorithm performance though evaluation of a matched filter. The experimental results are compared with theoretical expected values though the Night Vision Integrated Performance Model (NV-IPM). Additionally, we demonstrate the experimental results for a variety of imaging systems (IR, visible, and color), target sizes and orientations, as well as SNR values. Our results demonstrate how this detection sensitivity metric can be measured to provide additional insight into the final system performance.

Origami is the art of paper folding that allows a single flat piece of paper to assume different 3D shapes depending on the fold patterns and the sequence of folding. Using the principles of origami along with computation imaging technique the authors demonstrate a versatile shape‐morphing microwave imaging array with reconfigurable field‐of‐view and scene‐adaptive imaging capability. Microwave/millimeter‐wave based array imaging systems are expected to be the workhorse for sensory perception of future autonomous intelligent systems. The imaging capability of a planar array‐based systems operating in complex scattering conditions have limited field‐of‐view and lack the ability to adaptively reconfigure resolution. To overcome this, here, deviations from planarity and isometry are allowed, and a shape‐morphing computational imaging system is demonstrated. Implemented on a reconfigurable Waterbomb origami surface with 22 active metasurface panels that radiate near‐orthogonal modes across 17–27 GHz, capability to image complex 3D objects in full details minimizing the effects of specular reflections in diffraction‐limited sparse imaging with scene adaptability, reconfigurable cross‐range resolution, and field‐of‐view is demonstrated. Such electromagnetic origami surfaces, through simultaneous surface shape‐morphing ability (potentially with shape‐shifting electronic materials) and electromagnetic field programmability, opens up new avenues for intelligent and robust sensing and imaging systems for a wide range of applications.

Imaging systems using aspheric imaging lenses with complementary computation can deliver performance unobtainable in conventional imaging systems. These new imaging systems, termed Wavefront coded imaging systems, use specialized optics to capture a coded image of the scene. Decoding the intermediate image provides the "human-usable" image expected of an imaging system. Computation for the decoding step can be made completely transparent to the user with today's technology. Real-time Wavefront coded systems are feasible and cost-effective. This "computational imaging" technology can be adapted to solve a wide range of imaging problems. Solutions include the ability to provide focus-free imaging, to increase the field of view, to increase the depth of read, to correct for aberrations (even in single lens systems), and to account for assembly and temperature induced misalignment. Wavefront coded imaging has been demonstrated across a wide range of applications, including microscopy, miniature cameras, machine vision systems, infrared imaging systems and telescopes.

The analysis tools of traditional optical systems, such as modulation transfer functions, point spread functions, resolution test charts etc. are often not sufficient when analyzing computational imaging systems. Computational imaging systems benefit from the combined use of optics and electronics for accomplishing a given imaging or system task. In traditional optical systems the goal is essentially to form images that precisely depict a given object. Electronics are not required to form clear images, but could be required to analyze the images. In computational imaging systems specialized images are formed by generalized aspheric optical elements that are jointly optimized with the electronic processing. The specialized images formed at a detector are not necessarily clear images. Electronic processing is used to remove the image blur or otherwise form a final image. Computational imaging systems offer the advantage of increased performance and decreased size, weight, and cost over traditional optical systems. The Ambiguity Function (AF), traditionally used for the design of radar waveforms, plays an important role in computational imaging systems. The AF provides a concise analysis of the optical transfer functions of imaging systems over defocus. The Wigner Distribution (WD), traditionally used for the design of time-varying systems, is related to the AF and provides a concise analysis of the point spread functions (PSF) of imaging systems over defocus. We will describe the relationships and utility of these functions to computational imaging systems.

Metasurface has drawn extensive attention due to its capability of modulating light with a high degree of freedom through ultrathin and sub-wavelength optical elements, and metalens, as one of its important applications, promises to replace the bulky refractive optics, facilitating the imaging system light-weight and compact characteristics. Besides, computer-generated holography (CGH) is of substantial interest for three-dimensional (3D) imaging technology by virtue of its ability of restoring the whole optical wave field and re-constructing the true 3D scene. Consequently, the combination of metalens and CGH holds transformative potential in enabling the miniaturization of 3D imaging systems. However, its imaging performance is subject to the aberrations and speckle noises originating from the metalens and CGH. Inspired by recent progress that computational imaging can be applied to close the gap, a novel full-color imaging system, adopting end-to-end joint optimization of metalens and CGH for high imaging quality, is proposed in this paper. The U-net based network as the pre-processing adjusts weights to make the holographic reconstruction offset imaging defects, incorporating the imaging processing into the step of generating hologram. Optimized by deep learning, the proposed imaging system is capable of full-color imaging with high fidelity in a compact form factor, envisioned to take an essential step towards the high-performance miniaturized imaging system.

In this paper, a preliminary study related to the detection of breast cancer based on a computational microwave imaging system is presented at K-band frequencies. In comparison to normal tissues, the different dielectric properties (permittivity and conductivity) of the malignant tissues can be exploited in order to detect the presence of tumour through a microwave imaging system. This work demonstrates the detection of breast tumour as an application of a computational imaging technique by leveraging the concept of the dynamic metasurface antenna (DMA) aperture as a transmitter. In this framework, the computational imaging aperture can provide a compact system design with a low-cost deployment for detecting tumours in early-stage, and this can speed up the screening process of the population at risk. Particularly, early detection of Stage 1 can help with the determination of the best treatment method and enhance a patient's prognosis.

Computational imaging modalities support a simplification of the active architectures required in an imaging system and these approaches have been validated across the electromagnetic spectrum. Recent implementations have utilized pseudo-orthogonal radiation patterns to illuminate an object of interest-notably, frequency-diverse metasurfaces have been exploited as fast and low-cost alternative to conventional coherent imaging systems. However, accurately measuring the complex-valued signals in the frequency domain can be burdensome, particularly for sub-centimeter wavelengths. Here, computational imaging is studied under the relaxed constraint of intensity-only measurements. A novel 3D imaging system is conceived based on 'phaseless' and compressed measurements, with benefits from recent advances in the field of phase retrieval. In this paper, the methodology associated with this novel principle is described, studied, and experimentally demonstrated in the microwave range. A comparison of the estimated images from both complex valued and phaseless measurements are presented, verifying the fidelity of phaseless computational imaging.

Conventional wisdom in model-based computational imaging incorporates physics-based imaging models, noise characteristics, and image priors into a unified Bayesian framework. Rapid advances in deep learning have inspired a new generation of data-driven computational imaging systems with performances even better than those of their model-based counterparts. However, the design of learning-based algorithms for computational imaging often lacks transparency, making it difficult to optimize the entire imaging system in a complete manner.

To obtain a high imaging frame rate, a computational ghost imaging system scheme is proposed based on optical fiber phased array (OFPA). Through high-speed electro-optic modulators, the randomly modulated OFPA can provide much faster speckle projection, which can be precomputed according to the geometry of the fiber array and the known phases for modulation. Receiving the signal light with a low-pixel APD array can effectively decrease the requirement on sampling quantity and computation complexity owing to the reduced data dimensionality while avoiding the image aliasing due to the spatial periodicity of the speckles. The results of analysis and simulation show that the frame rate of the proposed imaging system can be significantly improved compared with traditional systems.

Abstract. As remote sensing and variable-rate technology are becoming more available for aerial applicators, practical methodologies for effective integration of these technologies are needed for site-specific aerial applications of crop production and protection materials. The objectives of this study were to demonstrate how to integrate an airborne multispectral imaging system and an IntelliStar variable-rate aerial application system for site-specific management of the winter weed henbit. The airborne imaging system was used to acquire natural color and near-infrared (NIR) images of a fallow field near College Station, Texas, for mapping the infestation of henbit shortly before herbicide application. The images were then rectified, classified, and converted to a binary prescription map with the full application rate on infested areas and the zero rate on non-infested areas. The variable-rate aerial application system mounted on an agricultural aircraft was used to apply glyphosate over the field based on the prescription map. Airborne imagery was collected 14 days after the herbicide application. The as-applied map along with the prescription map and the post-application imagery were used to assess the performance of the site-specific application. Spatial and statistical analysis results showed that the imaging system was effective for mapping henbit infestations and for assessing the performance of site-specific herbicide application, and that the variable-rate system accurately delivered the product at the desired rate to the prescribed areas for effective control of the weed. The methodology and results from this study will be useful for aerial applicators to incorporate airborne imaging and variable-rate application systems into their aerial application business to increase their capabilities and profits. Keywords: Aerial application, As-applied map, Henbit, Imaging system, Prescription map, Variable-rate technology.

Breakthroughs brought about by two‐dimensional (2D) materials in the field of photodetection have opened up new possibilities in infrared imaging. However, challenges still exist in fabricating high‐density detector arrays using such materials, which are essential for traditional imaging systems. In this study, we present a state‐of‐the‐art computing imaging system that utilizes a MoTe2/Si self‐powered photodetector coupled with flexible Hadamard modulation algorithms. This system demonstrates remarkable capability to produce high‐quality images in the shortwave infrared (SWIR) band, surpassing the capabilities of devices based on alternative material systems. The exceptional infrared imaging capability primarily stems from the MoTe2/Si photodetector's inherent features, including an ultra‐wide spectral range (265–1550 nm) and extremely high sensitivity (linear dynamic range (LDR) up to 123 dB, responsivity (R) up to 0.33 A W–1, external quantum efficiency (EQE) up to 43% and a specific detectivity (D*) exceeding 2.9 × 1011 Jones). Moreover, the imaging system demonstrates the ability to achieve high‐quality edge imaging of objects in the SWIR band (1550 nm), even in strong scattering environments and under low sampling rate conditions (sampling rate of 25%). We believe that this work will effectively advance the application scope of 2D materials in the field of computational imaging in SWIR bands.image

Super-oscillatory (SO) imaging presents a passive solution to the sub-diffraction imaging challenge. The point spread functions (PSFs) of SO systems are band-limited and locally oscillate faster than their highest Fourier frequency. Because of this property, SO imaging systems are not bound by the Nyquist limit, allowing them to acquire details finer than the diffraction limit. In this work, we present a comprehensive theoretical analysis of passive incoherent SO imaging, leading to two key results. First, we show that the SO property of an imaging system is preserved when the system is combined with a general system of lenses. This opens the door for integrating SO imaging into existing microscopes and telescopes. Second, we show that incoherent SO imaging cannot resolve feature sizes below λ / 2 because of the inability to achieve incoherence at finer scales. This establishes the operational limits of the approach. We demonstrate our theory experimentally with a compound SO system that achieves sub-wavelength resolutions using high-NA lenses and Fourier spectrum modulation.

Diffractive lens (DLs) computational imaging promises lightweight infrared imaging. However, it is still challenging to accurately obtain the degradation of DLs computational imaging systems to achieve high-quality imaging. Degradation estimation methods based on image-to-image are the most feasible way, but existing methods face the problem of low accuracy for severe degradation estimation. In this paper, we proposed a severe degradation estimation framework based on KernelNet, and realized the improvement of the accuracy of severe degenerate estimation by more than 1.13 dB. Furthermore, DyUnet, an image restoration network, was proposed, which is based on dynamic convolution. The results of the mid-wave diffractive lens infrared computational imaging system indicated that our method achieved high-quality imaging results comparable to the conventional one, while the number of optical elements was reduced from 7 to 2, and the weight was reduced by 50%.

Computational imaging techniques that rely on a compressed set of measurements and exploit prior information such as target size, scene sparsity, transceiver radiation pattern, etc are rapidly gaining popularity in areas such as medical and security imaging, remote sensing, and automotive radar as they can significantly reduce SWAP-C (Size, Weight, Power, and Cost) of hardware modules, especially at millimeter-wave frequencies. In this article, we propose using the covariance matrix of a large ensemble of representative targets to form a diagonalizing basis in which the transformed scene voxels are uncorrelated. In this basis, we introduce a method of image reconstruction, Covariance Likelihood based Regularization (CLR), where transformed voxels, with low likelihood according to the ensemble statistics, are penalized. We also discuss another method, Thresholded Eigenbasis (TE), which involves thresholding the eigenvalues of the covariance matrix and reconstructing the transformed scene voxels in a lower dimensional approximate basis. We use these techniques to reconstruct images from simulations of measurements made using a W-band (75 - 110 GHz) imaging system, where the linear imaging matrix is carefully designed based on vector electromagnetics and realistic hardware. Based on these reconstruction results, we discuss the opportunities and challenges for these methods, including scenarios where TE provides improved reconstruction speed and CLR provides improved accuracy.

Manufacturing diffractive lenses with a high numerical aperture (NA) is often a challenging task. The challenge stems from the fundamental limit of lithography techniques and the diffraction limit. Photolithography and femtosecond ablation are some of the well-established rapid lithography techniques for manufacturing large-area diffractive lenses for the visible region. First, when high NA diffractive lenses are designed, the outermost width of the zone becomes a sub-lithography limit (~ 2 μm) while still being super-wavelength. In advanced photolithography and most femtosecond ablation methods, the lithography limit is sub-wavelength, but scalar diffraction is not applicable, and the device becomes polarization sensitive. In this study, a holographic solution to overcome the above limitations is proposed. Fresnel incoherent correlation holography (FINCH) is a super-resolution incoherent imaging technique. In this project, a FINCH-inspired optical configuration is proposed to image beyond the lithography and diffraction limit of the diffractive lens. In a regular imaging system, the light from an object is collected by a diffractive lens and imaged, and recorded by an image sensor in the image plane. In this work, the intensity distribution is not recorded at the image plane but at a plane where the light modulated by the diffractive lens interferes with the unmodulated light outside the diffractive lens. This intensity distribution has spatial frequencies beyond the limit of the NA of the diffractive lens, resulting in super-resolution. Using the newly developed Lucy-Richardson-Rosen algorithm (LR²A), the image is reconstructed. We believe that the developed technique will improve the performance of imaging systems based on high-NA diffractive lenses.