High-Quality Diffractive lens Infrared Computational Imaging via Severe Degradation Estimation Framework

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.

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.

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.

Computational imaging is a proven strategy for obtaining high-quality images with fast acquisition rates and simpler hardware. Metasurfaces provide exquisite control over electromagnetic fields, enabling the radiated field to be molded into unique patterns. The fusion of these two concepts can bring about revolutionary advances in the design of imaging systems for security screening. In the context of computational imaging, each field pattern serves as a single measurement of a scene; imaging a scene can then be interpreted as estimating the reflectivity distribution of a target from a set of measurements. As with any computational imaging system, the key challenge is to arrive at a minimal set of measurements from which a diffraction-limited image can be resolved. Here, we show that the information content of a frequency-diverse metasurface aperture can be maximized by design, and used to construct a complete millimeter-wave imaging system spanning a 2 m by 2 m area, consisting of 96 metasurfaces, capable of producing diffraction-limited images of human-scale targets. The metasurfacebased frequency-diverse system presented in this work represents an inexpensive, but tremendously flexible alternative to traditional hardware paradigms, offering the possibility of low-cost, real-time, and ubiquitous screening platforms.

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.

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.

Computational ghost imaging system with 4-connected-region-optimized Hadamard pattern sequence

In this paper, we propose an improved analysis on the signal property of curved computational integral imaging reconstruction (C-CIIR). In the proposed model and analysis, we explain a general analysis of computational integral imaging by introducing a curvature effect that is obtained by the additional use of a large-aperture (LA) lens. Based on the proposed signal model in C-CIIR, we analyze the characteristics of the granular noise (GN) and conduct preliminary experiments to show the feasibility of our model. Experimental results indicate that the GN caused by the nonuniform overlapping gets reduced and that the GN is diminished as the focal length of the additional LA lens used decreases in C-CIIR. Also, the proposed model and analysis are considered to be generalized versions of the signal model and analysis of the previous computational integral imaging systems.

Comparison of computer-assisted and manual screening of cervical cytology

The mitigation of atmospheric effects is an essential component of any high-throughput optical satellite downlink. In this work, as part of the High-throughput and Secure Networks Challenge Program, we apply computational imaging approaches to turbulent wavefront estimation by capturing single pixel images of the speckle pattern from a simulated turbulence-corrupted wavefront. In particular, Hadamard patterns are sequentially displayed on a digital micromirror device (DMD) and used to reconstruct the turbulent speckle pattern. This speckle pattern can be used as an input to a phase-retrieval algorithm to estimate the wavefront phase that produced the speckle. We present the concepts of computational turbulent speckle imaging along with an experimental setup to demonstrate this approach. A spatial light modulator (SLM) is used to simulate turbulence-degraded wavefronts. These are then imaged onto a computational imaging system implemented with a DMD and balanced detection using two photodiodes. The experimental setup is described along with the image reconstruction algorithm. Preliminary computational speckle images are presented and compared with the predicted image obtained by numerically simulating the beam propagation through the optical system showing close agreement.

By using the dispersion Fourier transformation, a novel computational spectral ghost imaging system based on time-to-frequency mapping is proposed, which can simultaneously improve the spectral detection resolution and reduce the modulation times.

Influence of detector response speed on the contrast-to-noise ratio of reflective ghost imaging

Recent researches showed that the traditional ghost imaging (TGI) using pseudo-thermal light can be affected by the solid scattering material placed between the object and the light source. This scattering case might also affect the computational ghost imaging (CGI) and is considered in this paper. Different from the TGI system modeled by Huygens–Fresnel theory in continuous coordinates, in CGI the use of computer-generated patterns with the controllable size ( $d$ ) of each pixel discretizes the object space into the $d$ -based pixel-grids. Therefore, how the CGI system is affected by the scatters depends on the distribution of the scattered light on these discretized grids instead of in continuous coordinates, which has not been explored. In this paper, we demonstrate that the “anti-scattering” capacity of CGI is governed by the discrete point-scattering-function (DPSF) that is determined by $\frac{{w_{0}}}{d}$ ( $w_{0}$ : the width of point-scattering-function of the scatters). We show that the blurry effect on the reconstructed image is visible as $d\ll\frac{{w_{0}}}{2}$ , but can be highly restrained as $d$ increasing to be comparable to $\frac{w_{0}}{2}$ or larger. Furthermore, $d$ determines the spatial resolution power of CGI. In the given CGI system, the minimum $d$ of $\sim\frac{{w}_{0}}{5}$ could be achieved with maintaining the behavior of “scattering-free”.

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.

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.