Advanced Microwave Imaging

This paper describes a performance metric for the evaluation of active coherent imaging systems. This metric can be determined for any system using analytical considerations or measured using standard targets. It has implications for comparison of different imaging systems, optimization of imaging systems, and identification of areas in which significant improvements in particular systems can be realized. The imaging system performance metric described here is suitable for analysis of unconventional imaging systems in which the image is sensed by an array of discrete detectors or in which the image is produced by manipulation of arrays of data. The implications of this formula for determination of photon-efficient and optimized systems are discussed. An important result of this paper will be to show that the efficient use of photons is only part of the story. An efficient system must still be optimized to make best use of the imaging hardware.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A millimeter-wave (mmW) 3-D imaging system using high scanning-rate leaky-wave antennas in a frequency-diverse multiple-input–multiple-output (MIMO) antenna configuration is proposed. A novelty of the approach is the use of high scanning-rate leaky-wave antennas combined with MIMO to obtain both frequency and spatial diversities. Furthermore, an extended Rytov approximation (xRA), recently shown to provide accurate reconstructions for high permittivity and electrically large-sized low-loss objects, is also included in the approach. Compared with frequency-diversity-only and spatial-diversity-only imaging systems, the proposed system can provide enhanced imaging performance by leveraging both spatial and frequency diversities simultaneously. In addition, the imaging time and hardware cost can be significantly reduced, resulting in a low-cost mmW imaging system. The formulation for the 3-D imaging system is simplified to a scalar form by leveraging the polarization of the leaky-wave antennas and the polarization dominance of the scattered wave components, allowing xRA to be formulated. Sensing capacity and correlation coefficient evaluation methods are used to estimate the performance of the high scanning-rate leaky-wave MIMO imaging system. The scalar formulation also allows us to straightforwardly compare MIMO microwave imaging with MIMO radar to highlight the commonality between them. Numerical and experimental examples demonstrate that the proposed system with xRA can accurately estimate the contrast function amplitude and the positions of dielectric scatterers in a 3-D imaging region using two transmit and 11 receive leaky-wave antennas and 49 frequency samples within the 37.2–42-GHz frequency range.

The Square Integral Method based on the Minimum Resolvable Contrast (MRC) is to be introduced in this paper as an evolution for the design and evaluation of optoelectronic imaging systems. It is well known that there exists an optimal angle magnification which can make optoelectronic imaging systems and human eye matching optimally, so that optoelectronic imaging systems can performance best. Based on MRC (Minimum Resolvable Contrast) and channel width, a new method called Square Integral (SQI) method was presented for evaluating the general performance of a CCD imaging system, and attaining the optimal angle magnification or optimal viewing distance. Results calculated with this method are in good agreement with the experimental measurements. From the agreement between the practical use and the theoretical predictions for the variation of CCD size, optical focus, luminance and human vision, it demonstrates that the SQI method is an excellent universal measure for the optimal angle magnification and the performance of CCD imaging 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.

A typical problem encountered on a beamline with a sagittal focusing monochromator is the difficulty of optimizing the crystal alignment because the full width of the monochromatic, unfocused beam cannot be visualized. This problem is particularly acute on beamlines that accept a wide swath of radiation combined with a fixed experimental setup consisting of constrained apertures that only allow the focused beam to pass through to the sample position. To address this problem, an imaging system has been designed and implemented on the GM/CA bending magnet beamline at the Advanced Photon Source, Argonne National Laboratory. This imaging system consists of a 30mm × 50mm flat YAG crystal, right angle prism/mirror and video camera located downstream of the vertical collimating mirror, sagittally focusing monochromator and vertical focusing mirror. The YAG crystal was installed in a 6-way vacuum cross in the beam path, and was mounted on a linear translation feedthrough allowing remote controlled insertion and removal of the crystal from the beam path. The CCD camera was mounted outside the vacuum chamber and views the x-ray induced scintillation from the YAG crystal through a window. This visualizer allows one to optimize the twist, yaw and roll of the second crystal, facilitates proper sagittal focusing and serves as a remote controlled diagnostic tool. An overview of the design and the performance of the imaging system will be presented.

The design of holographic acoustic imaging systems using hydrophone arrays at the Naval Undersea Center has led to the development of special signal processing electronics which will be described in detail. The tolerances on electronic component values in this circuit, the major noise sources in the circuit, and other properties of an imaging system will be used to calculate the expected imaging performance of a holographic imaging system, as measured by its image dynamic range. An alternative version of the circuit will also be discussed, analyzed, and compared with the original circuit. Finally, an analogy between the signal processing circuit and a cross-correlator will be made, indicating possible future developments.

A number of complementary metrics are available to assess the performance of digital X-ray imaging systems. However, the sensitivity of these metrics to changes in the electro-optical imaging chain is poorly understood. Some of the commonly used metrics include Contrast to Noise ratio (CNR), limiting spatial resolution, Modulation Transfer Function (MTF), Noise Power Spectrum (NPS) and the Detective Quantum Efficiency (DQE). We evaluated the utility of these metrics in characterizing the imaging plate, imaging system optics and electronic components of computed radiography (CR) systems. We developed practical and easy to use test objects (phantoms) and implemented software to aid in calculating each metric. The results of this research will facilitate the characterization of differences in CR systems using the appropriate metrics.

Medical images are acquired for the purpose of gathering information regarding the disease state of a patient. The quality of the image should be a measure of how a specified task may be performed given the resulting image. Image quality is task dependent; one imaging system may provide images that allow better task performance than another system's for one task, but worse for another task. Determining the quality of an imaging system involves specifying the task of interest, the range of objects that will be imaged, and the observer (human or machine reader) who will perform the task. In radiography it has become common to use Fourier measures to describe image quality through a combination of Fourier‐based measures of system resolution and noise. The Line Spread Function, when Fourier transformed, yields the Modulation Transfer Function (MTF) as a measure of system resolution. Use of the MTF to describe a system's resolution is appropriate when the imaging system is shift‐invariant, that is, the line spread function is independent of position. Image noise is described in the Fourier domain by the Noise Power Spectrum (NPS). We can use the NPS to describe the noise in an imaging system whenever the noise properties are stationary, meaning that the variance and correlations between fluctuations in different locations are not dependent on their absolute location. These Fourier measures of system resolution and noise are commonly combined to give an overall figure of merit known as the Detective Quantum Efficiency (DQE). The DQE describes the signal‐to‐noise transfer characteristics of an imaging system as long as those assumptions of shift‐invariance and noise stationarity are valid. In digital systems the image of an object is affected by the location of the object with respect to the pixel grid. In other words, the detector pixels cause the system to be shift‐variant. In this case the MTF does not describe the resolution properties completely. This is particularly the case for tomographic systems, where the point‐response function is quite position‐dependent. Moreover, in real imaging systems the noise properties will depend on position, most notably because real patients always have structure. So, the Fourier‐domain noise concept of Noise Power Spectrum does not capture all the properties of noise for real imaging systems and tasks. In this talk we will consider a general treatment of the imaging system as a mapping of information from the patient to the data, without the assumptions inherent in Fourier analysis. We will discuss approaches to the characterization of system resolution and noise in terms of position‐dependent parameters. Once such position‐dependent imaging properties of a system are determined, we can use them to calculate measures of image quality that summarize the usefulness of the images for the performance of various visual tasks.

A simple multispectral classification method for the identification of systemically diseased chickens was developed and tested between two different imaging systems. An image processing algorithm was developed to define and locate the region of interest (ROI) as classification areas on the image. The average intensity was calculated for each classification area of the chicken image. A decision tree algorithm was used to determine threshold values for each classification areas. The wavelength of 540 nm was used for image differentiation purpose. There were 164 wholesome and 176 systemically diseased chicken images collected using the first imaging system, and 332 wholesome and 318 systemically diseased chicken images taken by the second imaging system. The differentiation thresholds, generated by the decision tree method, based on the images from the first imaging system were applied to the images from the second imaging system, and vice versa. The accuracy from evaluation was 95.7% for wholesome and 97.7% of systemically diseased chickens for the first image batch, and 99.7% for wholesome and 93.5% for systemically diseased chickens for the second image batch. The result showed that using single wavelength and threshold, this simple classification method can be used in automated on-line applications for chicken inspection.

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.

Significant efforts are currently ongoing in X-Ray imaging to provide multimodal imaging systems, targeting better sensitivity and specificity for both biomedical or non-destructive testing (NDT) applications. X-Ray Phase Contrast Imaging (X-PCI) shows great capability to differentiate elements with similar absorption. For example, in the medical field, knowing the chemical composition of breast microcalcifications would help to differentiate malign and benign tumors. The composition can be determined from the measurement of the phase as the optical index of materials is directly related to the composition. We propose a novel, high-sensitivity X-ray quantitative phase imaging system based on a Hartmann wavefront sensor. The system provides high resolution (20?m without magnification) and high sensitivity (~100 nrad), and is compatible with tomographic experiments using both synchrotron beamlines or laboratory sources. We present here our first X-PCI prototype as well as the first images obtained. We also present an alternative design based on the same approach, providing larger field-of-view at the cost of some trade-off regarding resolution and sensitivity and the first tomographic results obtained with this imaging system.

In recent years, our research group has been devoting substantial efforts to the research and development of active all-solid-state electronic terahertz (THz) continuous wave imaging systems for nondestructive testing, which is currently benefitting from the increasing amount of transmitting power, high performance/cost ratio and adaptability to engineering. In this paper, an in-house developed broadband linear frequency modulated continuous wave (LFMCW) three-dimensional (3D) THz imaging system is described, and two sets of experimental platforms are set up to assist the planning and completion of the research, including a narrow-band LFMCW 3D imaging radar and a wide-band stepped-frequency modulated continuous wave 3D imaging radar. For 3D imaging systems, to cope with demanding scenarios and to achieve excellent imaging performance, various reconstruction algorithms are explored. The first is a spectral refinement and correction approach based on fast Fourier transform and modern spectral estimation for accurate thickness measurement. The second is the synthetic aperture radar imaging algorithm for surface detection or internal detection of objects with lower refractive index. The third is a 3D reconstruction algorithm based on half space Green's function and the exploding source model for the interior detection of materials with higher refractive index. The fourth is the frequency interference algorithm combining phase unwrapping to measure uneven and nonplanar surfaces. Exploiting these systems, along with the associated experimental platforms and reconstruction algorithms, we successfully implemented non-destructive testing for objects with various defects and of different materials, such as polymer boards with voids, and foam with inclusions.

Non-homogeneous, bi-directional glass fabric reinforced thermoplastic (GFRT) composites are used in the automobile industry to produce economic and environmentally friendly vehicles. Despite the fact that GFRT materials have superior mechanical characteristics and lower manufacturing cost compared with traditional materials, they are prone to significant deformation and defect emergence during the forming/manufacturing of parts from prefabricated laminates. Some defects, especially hidden ones like delamination and consolidation defects, can significantly reduce the mechanical properties of the final parts. It is essential to use 3D non-destructive testing (NDT) technology to detect and identify such hidden defects. This research investigates the ability of 3D THz imaging to detect hidden defects (i.e., delamination and consolidation) in GFRT composite materials. Frequency modulated continuous wave (FMCW) technique is used to generate 3D-THz cross-sectional images. Non-defective and defective samples with a thickness of 1.5mm have been measured with two 3D THz imaging systems operating in the frequency range from 238 to 316GHz and from 499 to 733 GHz. In addition, a simulation of the reflected FMCW spectrum is presented to analyze the measurements and intercompare the performance of the two imaging systems. A systematic analysis in a large sample set demonstrates that a delamination defect with a thickness of 125μm as well as a consolidation defect with a difference in thickness less than 0.4mm can be successfully detected by the 700-GHz imaging system.

The optical transfer function (OTF), which correspond to the frequency domain, is of fundamental importance in characterizing imaging ability in optical imaging systems. By using the Collins formula and the optical transfer matrix theory, a simple and unified analytical expression of OTF is presented in any ghost imaging (GI) system. By comparing the spectrum distribution of OTF between different GI systems, one can predict which imaging system can provide better imaging quality when the same imaging object is used. Theoretical and experimental results are also implemented to verify our results.

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.