arXiv subjects

David J. Brady

Publications and source records attributed to David J. Brady.

At least 19 recordsLinked to original sources

arXiv2026

Active Electronic Terahertz Imaging for Industrial Applications: From Hardware to the Paradigm Shift by Artificial Intelligence

Imaging with terahertz (THz) radiation (0.3-10 THz) benefits from a unique combination of attributes: penetration through dry, non-polar packaging materials; variations of dielectric functions to provide contrast; the existence of spectral fingerprint resonances for some classes of materials; non-ionizing photon energies that are safe for use around humans; and - viewed from the low-frequency side - an extension of the capabilities of microwave radar to higher frequencies and thus to substantially better spatial resolution, at wavelengths which still permit direct measurement of the complex-valued radiation field. This review concentrates on active THz imaging with electronic sources combined with power detectors or coherent receivers - the system class most likely to deliver fast (ideally real-time), cost-effective and deployable solutions for a wide range of industrial applications such as quality control, non-destructive testing, security screening and ranging for situational awareness. Such systems should be deployable on robotic and emerging autonomous platforms. We review the state of the art of compact semiconductor detector arrays, of imaging modalities ranging from focused-beam raster and frequency-modulated continuous-wave architectures to coherent Fourier-plane acquisition, and of augmentation techniques such as compressive sensing. Particular attention is paid to the growing role of artificial intelligence: from convolutional neural networks and physics-informed deep learning for phase retrieval and image reconstruction, to agentic frameworks for autonomous system design. The bottlenecks of THz imaging - acquisition speed, resolution, contrast and cost - are re-examined in the light of these innovations, and a reference-anchored technology roadmap is derived which projects an order-of-magnitude reduction in the measurement requirements of THz imaging.

physics.optics↗

arXiv2026

Dense Holographic Associative Memories

Associative recall -- mapping an incident pattern to the stored one it most resembles -- is the natural computational primitive of a high-dimensional vision front end, and it is precisely the operation a volume hologram performs natively. We show that a cascade of two volume holograms separated by a one-dimensional coded layer physically evaluates the modern Hopfield (dense associative memory) retrieval map, $η= V \text{softmax}(λK^T x)$, exactly as a parallel optical computation, with the inverse temperature realized via optically addressed spatial light modulation in the coded-layer. Routing the input and output through a 1D code rather than directly between 2D planes supplies the separating nonlinearity the original Hopfield model lacked and, by balancing the grating-wavevector dimension count ($2+1=3$), removes the Bragg degeneracy that otherwise forces fractal sampling on a direct 2D-to-2D hologram. Faithful dense storage further demands a recording medium that captures inter-neuron connections while rejecting the field self-energy responsible for the $M^{-2}$ efficiency falloff of homogeneous photorefractives. We propose a nonlocal, gradient-responsive medium whose illumination-independent decay recovers the linear $M^{-1}$ scaling in situ, and demonstrate its reception, combination, and storage functions in a discrete opposing-diode cell. Routes to OASLM-stack and volume molecular/nanocrystal realizations are outlined.

eess.IV↗

arXiv2025

Radiometric sensitivity and resolution of synthetic tracking imaging for orbital debris monitoring

We consider sampling and detection strategies for solar illuminated space debris. We argue that the lowest detectable debris cross section may be reduced by 10-100x by analysis of stacks of image frames collected at high rates rather than single frame data. In particular, instead of a pixel as a spatial region, the analysis is based on a "phase-space-pixel" which corresponds to an angular velocity and space region and whose intensity is computed by a weighted stacking of spatial pixels corresponding to a test debris trajectory within a wide camera field-of-view (FOV). To isolate debris signals from background, the exposure time is set to match the time it takes a debris to transit through the instantaneous field of view. Debris signatures are detected by multiscale X-ray processing of the data cube. Radiometric analysis of line integrals shows that sub-cm objects in Low Earth Orbit can be detected and assigned full orbital parameters by this approach.

astro-ph.EP↗

arXiv2024

Continuous 3D Myocardial Motion Tracking via Echocardiography

Myocardial motion tracking stands as an essential clinical tool in the prevention and detection of cardiovascular diseases (CVDs), the foremost cause of death globally. However, current techniques suffer from incomplete and inaccurate motion estimation of the myocardium in both spatial and temporal dimensions, hindering the early identification of myocardial dysfunction. To address these challenges, this paper introduces the Neural Cardiac Motion Field (NeuralCMF). NeuralCMF leverages implicit neural representation (INR) to model the 3D structure and the comprehensive 6D forward/backward motion of the heart. This method surpasses pixel-wise limitations by offering the capability to continuously query the precise shape and motion of the myocardium at any specific point throughout the cardiac cycle, enhancing the detailed analysis of cardiac dynamics beyond traditional speckle tracking. Notably, NeuralCMF operates without the need for paired datasets, and its optimization is self-supervised through the physics knowledge priors in both space and time dimensions, ensuring compatibility with both 2D and 3D echocardiogram video inputs. Experimental validations across three representative datasets support the robustness and innovative nature of the NeuralCMF, marking significant advantages over existing state-of-the-art methods in cardiac imaging and motion tracking.

eess.IV↗

arXiv2023

Large-scale Global Low-rank Optimization for Computational Compressed Imaging

Computational reconstruction plays a vital role in computer vision and computational photography. Most of the conventional optimization and deep learning techniques explore local information for reconstruction. Recently, nonlocal low-rank (NLR) reconstruction has achieved remarkable success in improving accuracy and generalization. However, the computational cost has inhibited NLR from seeking global structural similarity, which consequentially keeps it trapped in the tradeoff between accuracy and efficiency and prevents it from high-dimensional large-scale tasks. To address this challenge, we report here the global low-rank (GLR) optimization technique, realizing highly-efficient large-scale reconstruction with global self-similarity. Inspired by the self-attention mechanism in deep learning, GLR extracts exemplar image patches by feature detection instead of conventional uniform selection. This directly produces key patches using structural features to avoid burdensome computational redundancy. Further, it performs patch matching across the entire image via neural-based convolution, which produces the global similarity heat map in parallel, rather than conventional sequential block-wise matching. As such, GLR improves patch grouping efficiency by more than one order of magnitude. We experimentally demonstrate GLR's effectiveness on temporal, frequency, and spectral dimensions, including different computational imaging modalities of compressive temporal imaging, magnetic resonance imaging, and multispectral filter array demosaicing. This work presents the superiority of inherent fusion of deep learning strategies and iterative optimization, and breaks the persistent dilemma of the tradeoff between accuracy and efficiency for various large-scale reconstruction tasks.

eess.IV↗

arXiv2022

Space-Time Projection Optical Tomography: Search Space and Orbit Determination

In a companion article, we discussed the radiometric sensitivity and resolution of a new passive optical sensing technique, Space-Time Projection Optical Tomography (SPOT), to detect and track sub-cm and larger space debris for Space Situational Awareness. SPOT is based on the principle that long synthetic exposure can be achieved if the phase-space trajectory of a hypothetical point-source is precisely predictable within a very wide telescope field-of-view, which is the case for orbiting debris. This article discusses the computational search space for debris mining as well as a recursive measure-and-fit algorithm based on a generalized Hough transform for orbit determination.

physics.optics↗

arXiv2022

Scatter Ptychography

Coherent illumination reflected by a remote target may be secondarily scattered by intermediate objects or materials. Here we show that phase retrieval on remotely observed images of such scattered fields enables imaging of the illuminated object at resolution proportional to $λR_s/A_s$, where $R_s$ is the range between the scatterer and the target and $A_s$ is the diameter of the observed scatter. This resolution may exceed the resolution of directly viewing the target by the factor $R_cA_s/R_sA_c$, where $R_c$ is the range between the observer and the target and $A_c$ is the observing aperture. Here we use this technique to demonstrate $\approx 32\times$ resolution improvement relative to direct imaging.

eess.IV↗

arXiv2021

Snapshot Ptychography on Array cameras

We use convolutional neural networks to recover images optically down-sampled by $6.7\times$ using coherent aperture synthesis over a 16 camera array. Where conventional ptychography relies on scanning and oversampling, here we apply decompressive neural estimation to recover full resolution image from a single snapshot, although as shown in simulation multiple snapshots can be used to improve SNR. In place training on experimental measurements eliminates the need to directly calibrate the measurement system. We also present simulations of diverse array camera sampling strategies to explore how snapshot compressive systems might be optimized.

eess.IV↗

arXiv2021

Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision

Signal capture stands in the forefront to perceive and understand the environment and thus imaging plays the pivotal role in mobile vision. Recent explosive progresses in Artificial Intelligence (AI) have shown great potential to develop advanced mobile platforms with new imaging devices. Traditional imaging systems based on the "capturing images first and processing afterwards" mechanism cannot meet this unprecedented demand. Differently, Computational Imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems.Thanks to AI, CI can now be used in real systems by integrating deep learning algorithms into the mobile vision platform to achieve the closed loop of intelligent acquisition, processing and decision making, thus leading to the next revolution of mobile vision.Starting from the history of mobile vision using digital cameras, this work first introduces the advances of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Motivated by the fact that most existing studies only loosely connect CI and AI (usually using AI to improve the performance of CI and only limited works have deeply connected them), in this work, we propose a framework to deeply integrate CI and AI by using the example of self-driving vehicles with high-speed communication, edge computing and traffic planning. Finally, we outlook the future of CI plus AI by investigating new materials, brain science and new computing techniques to shed light on new directions of mobile vision systems.

cs.CV↗

arXiv2021

Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications

Capturing high-dimensional (HD) data is a long-term challenge in signal processing and related fields. Snapshot compressive imaging (SCI) uses a two-dimensional (2D) detector to capture HD ($\ge3$D) data in a {\em snapshot} measurement. Via novel optical designs, the 2D detector samples the HD data in a {\em compressive} manner; following this, algorithms are employed to reconstruct the desired HD data-cube. SCI has been used in hyperspectral imaging, video, holography, tomography, focal depth imaging, polarization imaging, microscopy, \etc.~Though the hardware has been investigated for more than a decade, the theoretical guarantees have only recently been derived. Inspired by deep learning, various deep neural networks have also been developed to reconstruct the HD data-cube in spectral SCI and video SCI. This article reviews recent advances in SCI hardware, theory and algorithms, including both optimization-based and deep-learning-based algorithms. Diverse applications and the outlook of SCI are also discussed.

eess.IV↗

arXiv2021

Thin Lenses and Thin Cameras

Cassegrain designs can be used to build thin lenses. We analyze the relationships between system thickness and aperture sizes of the two mirrors as well as FoV size. Our analysis shows that decrease in lens thickness imposes tight constraint on the aperture and FoV size. To mitigate this limitation, we propose to fill the gaps between the primary and the secondary with high index material. The Gassegrain optics cuts the track length into half and high index material reduces ray angle and height, consequently the incident ray angle can be increased, i.e., the FoV angle is extended. Defining telephoto ratio as the ratio of lens thickness to focal length, we achieve telephoto ratios as small as 0.43 for a visible Cassegrain thin lens and 1.20 for an infrared Cassegrain thin lens. To achieve an arbitrary FoV coverage, we present an strategy by integrating multiple thin lenses on one plane with each unit covering a different FoV region. To avoid physically tilting each unit, we propose beam steering with metasurface. By image stitching, we obtain wide FoV images.

physics.optics↗

arXiv2020

Multiscale Phase Retrieval

While characterization of coherent wavefields is essential to laser, x-ray and electron imaging, sensors measure the squared magnitude of the field, rather than the field itself. Holography or phase retrieval must be used to characterize the field. The need for a reference severely restricts the utility of holography. Phase retrieval, in contrast, is theoretically consistent with sensors that directly measure coherent or partially coherent fields with no prior assumptions. Unfortunately, phase retrieval has not yet been successfully implemented for large-scale fields. Here we show that both holography and phase retrieval are capable of quantum-limited coherent signal estimation and we describe phase retrieval strategies that approach the quantum limit for >1 megapixel fields. These strategies rely on group testing using networks of interferometers, such as might be constructed using emerging integrated photonic, plasmonic and/or metamaterial devices. Phase-sensitive sensor planes using such devices could eliminate the need both for lenses and reference signals, creating a path to large aperture diffraction limited laser imaging.

eess.IV↗

arXiv2020

Intelligent Autofocus

We demonstrate that deep learning methods can determine the best focus position from 1-2 image samples, enabling 5-10x faster focus than traditional search-based methods. In contrast with phase detection methods, deep autofocus does not require specialized hardware. In further constrast with conventional methods, which assume a static "best focus," AI methods can generate scene-based focus trajectories that optimize synthesized image quality for dynamic and three dimensional scenes.

eess.IV↗

arXiv2020

Smart Cameras

We review camera architecture in the age of artificial intelligence. Modern cameras use physical components and software to capture, compress and display image data. Over the past 5 years, deep learning solutions have become superior to traditional algorithms for each of these functions. Deep learning enables 10-100x reduction in electrical sensor power per pixel, 10x improvement in depth of field and dynamic range and 10-100x improvement in image pixel count. Deep learning enables multiframe and multiaperture solutions that fundamentally shift the goals of physical camera design. Here we review the state of the art of deep learning in camera operations and consider the impact of AI on the physical design of cameras.

eess.IV↗

arXiv2019

Distributed Focus and Digital Zoom

We explore integrated microcamera focus systems for array cameras. We propose a new model for system camera integration relying on fast action focus mechanisms with >10mm aperture. Rather than reducing resolution or expanding aperture size, such systems can be used in arrays to enable digital zoom. We show that a common mechanism supports camera modules with focal lengths ranging from 25 to 60 mm. Designs for each focal length include a fixed objective lens group and an adjustable back focus group. Increasing the focal power of the front focal group enables the travel range of available microcamera modules to accommodate long focal length systems. We present design examples both discrete and multiscale array camera systems.

eess.IV↗

arXiv2019

Compressive Sampling for Array Cameras

While design of high performance lenses and image sensors has long been the focus of camera development, the size, weight and power of image data processing components is currently the primary barrier to radical improvements in camera resolution. Here we show that Deep-Learning- Aided Compressive Sampling (DLACS) can reduce operating power on camera-head electronics by 20x. Traditional compressive sampling has to date been primarily applied in the physical sensor layer, we show here that with aid from deep learning algorithms, compressive sampling offers unique power management advantages in digital layer compression.

eess.IV↗

arXiv2018

Rank Minimization for Snapshot Compressive Imaging

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications.This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.

cs.CV↗

arXiv2018

Perceptual Quality Assessment of Immersive Images Considering Peripheral Vision Impact

Conventional images/videos are often rendered within the central vision area of the human visual system (HVS) with uniform quality. Recent virtual reality (VR) device with head mounted display (HMD) extends the field of view (FoV) significantly to include both central and peripheral vision areas. It exhibits the unequal image quality sensation among these areas because of the non-uniform distribution of photoreceptors on our retina. We propose to study the sensation impact on the image subjective quality with respect to the eccentric angle $θ$ across different vision areas. Often times, image quality is controlled by the quantization stepsize $q$ and spatial resolution $s$, separately and jointly. Therefore, the sensation impact can be understood by exploring the $q$ and/or $s$ in terms of the $θ$, resulting in self-adaptive analytical models that have shown quite impressive accuracy through independent cross validations. These models can further be applied to give different quality weights at different regions, so as to significantly reduce the transmission data size but without subjective quality loss. As demonstrated in a gigapixel imaging system, we have shown that the image rendering can be speed up about 10$\times$ with the model guided unequal quality scales, in comparison to the the legacy scheme with uniform quality scales everywhere.

cs.MM↗