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).

Compressive video via IR-pulsed illumination.

We propose and demonstrate a compressive temporal imaging system based on pulsed illumination to encode temporal dynamics into the signal received by the imaging sensor during exposure time. Our approach enables >10x increase in effective frame rate without increasing camera complexity. To mitigate the complexity of the inverse problem during reconstruction, we introduce two keyframes: one before and one after the coded frame.

Snapshot ptychography on array cameras.

We use convolutional neural networks to recover images optically down-sampled by 6.7 × 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 signal-to-noise ratio (SNR).

Review of bio-optical imaging systems with a high space-bandwidth product.

Optical imaging has served as a primary method to collect information about biosystems across scales-from functionalities of tissues to morphological structures of cells and even at biomolecular levels. However, to adequately characterize a complex biosystem, an imaging system with a number of resolvable points, referred to as a space-bandwidth product (SBP), in excess of one billion is typically needed. Since a gigapixel-scale far exceeds the capacity of current optical imagers, compromises must be made to obtain either a low spatial resolution or a narrow field-of-view (FOV).

Photon-limited bounds for phase retrieval.

We show that the optimal Cramér-Rao lower bound on the mean-square error for the estimation of a coherent signal from photon-limited intensity measurements is equal to the number of signal elements, or the number of signal elements minus one when we account for the unobservable reference phase. Whereas this bound is attained by phase-quadrature holography, we also show that it can be attained through a phase-retrieval system that does not require a coherent reference. We also present the bounds for classic phase-retrieval and ptychography, and show that practical coding strategies can approach optimal performance.

A modular hierarchical array camera.

Array cameras removed the optical limitations of a single camera and paved the way for high-performance imaging via the combination of micro-cameras and computation to fuse multiple aperture images. However, existing solutions use dense arrays of cameras that require laborious calibration and lack flexibility and practicality. Inspired by the cognition function principle of the human brain, we develop an unstructured array camera system that adopts a hierarchical modular design with multiscale hybrid cameras composing different modules.

Noise suppression for ballistic-photons based on compressive in-line holographic imaging through an inhomogeneous medium.

Noise suppression is one of the most important tasks in imaging through inhomogeneous mediums. Here, we proposed a denoising approach based on compressive in-line holography for imaging through an inhomogeneous medium. A reference-beam-free system with a low-cost continuous-wave laser is presented.

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.

Twin-Image-Free Holography: A Compressive Sensing Approach.

Holographic reconstruction is troubled by the phase-conjugate wave front arising from Hermitian symmetry of the complex field. The so-called twin image obfuscates the reconstruction in solving the inverse problem. Here we quantitatively reveal how and how much the twin image affects the reconstruction and propose a compressive sensing (CS) approach to reconstruct a hologram completely free from the twin image.

Field of view in monocentric multiscale cameras.

Conventionally, the field of view of a camera is understood as the angular extent of a convex circular or rectangular region. Parallel camera architectures with computational image stitching, however, allow implementation of a field of view with an arbitrary shape. Monocentric multiscale lenses further allow the implementation of an arbitrary field of view in camera volumes comparable to conventional single-lens systems.

Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging.

In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.

Galilean monocentric multiscale optical systems.

The first generation of monocentric multiscale gigapixel cameras used Keplerian designs to enable full field coverage. This paper considers alternative designs that remove the requirement that adjacent subimages overlap. Removing this constraint enables Galilean designs that reduce system volume and improve relative illumination and image quality.

Efficient block-wise algorithm for compressive holography.

Compressive holography is a relatively time-consuming image estimation in convex optimized problem. We propose an efficient block-wise algorithm to limit the searching space and reduce the calculation time while keeping the reconstruction quality. The effective anti-aliasing boundary of the sub-hologram is located to determine the block size for compressive reconstruction in the total-variation two-step iterative shrinkage/thresholding algorithm.

Heterogeneous camera array for multispectral light field imaging.

Multispectral light field acquisition is challenging due to the increased dimensionality of the problem. In this paper, inspired by anaglyph theory (i.e.

Coded Apertures in Mass Spectrometry.

The use of coded apertures in mass spectrometry can break the trade-off between throughput and resolution that has historically plagued conventional instruments. Despite their very early stage of development, coded apertures have been shown to increase throughput by more than one order of magnitude, with no loss in resolution in a simple 90-degree magnetic sector. This enhanced throughput can increase the signal level with respect to the underlying noise, thereby significantly improving sensitivity to low concentrations of analyte.

Snapshot fan beam coded aperture coherent scatter tomography.

We use coherently scattered X-rays to measure the molecular composition of an object throughout its volume. We image a planar slice of the object in a single snapshot by illuminating it with a fan beam and placing a coded aperture between the object and the detectors. We characterize the system and demonstrate a resolution of 13 mm in range and 2 mm in cross-range and a fractional momentum transfer resolution of 15%.

Spectral-temporal compressive imaging.

This Letter presents a compressive camera that integrates mechanical translation and spectral dispersion to compress a multi-spectral, high-speed scene onto a monochrome, video-rate detector. Experimental reconstructions of 17 spectral channels and 11 temporal channels from a single measurement are reported for a megapixel-scale monochrome camera.

Single-sensor multispeaker listening with acoustic metamaterials.

Designing a "cocktail party listener" that functionally mimics the selective perception of a human auditory system has been pursued over the past decades. By exploiting acoustic metamaterials and compressive sensing, we present here a single-sensor listening device that separates simultaneous overlapping sounds from different sources. The device with a compact array of resonant metamaterials is demonstrated to distinguish three overlapping and independent sources with 96.

Order of Magnitude Signal Gain in Magnetic Sector Mass Spectrometry Via Aperture Coding.

Miniaturizing instruments for spectroscopic applications requires the designer to confront a tradeoff between instrument resolution and instrument throughput [and associated signal-to-background-ratio (SBR)]. This work demonstrates a solution to this tradeoff in sector mass spectrometry by the first application of one-dimensional (1D) spatially coded apertures, similar to those previously demonstrated in optics. This was accomplished by replacing the input slit of a simple 90° magnetic sector mass spectrometer with a specifically designed coded aperture, deriving the corresponding forward mathematical model and spectral reconstruction algorithm, and then utilizing the resulting system to measure and reconstruct the mass spectra of argon, acetone, and ethanol.

Spatial light modulator based color polarization imaging.

We describe a compressive snapshot color polarization imager that encodes spatial, spectral, and polarization information using a liquid crystal modulator. We experimentally show that polarization imaging is compressible by multiplexing polarization states and present the reconstruction results. This compressive camera captures the spatial distribution of four polarizations and three color channels.

Two-dimensional aperture coding for magnetic sector mass spectrometry.

In mass spectrometer design, there has been a historic belief that there exists a fundamental trade-off between instrument size, throughput, and resolution. When miniaturizing a traditional system, performance loss in either resolution or throughput would be expected. However, in optical spectroscopy, both one-dimensional (1D) and two-dimensional (2D) aperture coding have been used for many years to break a similar trade-off.

Metamaterial microwave holographic imaging system.

We demonstrate a microwave imaging system that combines advances in metamaterial aperture design with emerging computational imaging techniques. The flexibility inherent to guided-wave, complementary metamaterials enables the design of a planar antenna that illuminates a scene with dramatically varying radiation patterns as a function of frequency. As frequency is swept over the K-band (17.

Compressive sensing by learning a Gaussian mixture model from measurements.

Compressive sensing of signals drawn from a Gaussian mixture model (GMM) admits closed-form minimum mean squared error reconstruction from incomplete linear measurements. An accurate GMM signal model is usually not available a priori, because it is difficult to obtain training signals that match the statistics of the signals being sensed. We propose to solve that problem by learning the signal model in situ, based directly on the compressive measurements of the signals, without resorting to other signals to train a model.

Compressed sampling strategies for tomography.

We investigate new sampling strategies for projection tomography, enabling one to employ fewer measurements than expected from classical sampling theory without significant loss of information. Inspired by compressed sensing, our approach is based on the understanding that many real objects are compressible in some known representation, implying that the number of degrees of freedom defining an object is often much smaller than the number of pixels/voxels. We propose a new approach based on quasi-random detector subsampling, whereas previous approaches only addressed subsampling with respect to source location (view angle).

Video compressive sensing using Gaussian mixture models.

A Gaussian mixture model (GMM)-based algorithm is proposed for video reconstruction from temporally compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method benefits from online adaptive learning and parallel computation.