Edge-resolved transient imaging (ERTI) is a method for non-line-of-sight imaging that combines the use of direct time of flight for measuring distances with the azimuthal angular resolution afforded by a vertical edge occluder. Recently conceived and demonstrated for the first time, no performance analyses or optimizations of ERTI have appeared in published papers. This paper explains how the difficulty of detection of hidden scene objects with ERTI depends on a variety of parameters, including illumination power, acquisition time, ambient light, visible-side reflectivity, hidden-side reflectivity, target range, and target azimuthal angular position. Based on this analysis, optimization of the acquisition process is introduced whereby the illumination dwell times are varied to counteract decreasing signal-to-noise ratio at deeper angles into the hidden volume. Inaccuracy caused by a coaxial approximation is also analyzed and simulated.
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Quantitative signal subspace imaging
We develop and analyze a quantitative signal subspace imaging method for single-frequency array imaging. This method is an extension to multiple signal classification which uses (i) the noise subspace to determine the location and support of targets, and (ii) the signal subspace to recover quantitative information about the targets. For point targets, we are able to recover the complex reflectivity and for an extended target under the Born approximation, we are able to recover a scalar quantity that is related to the product of the volume and relative dielectric permittivity of the target. Our resolution analysis for a point target demonstrates this method is capable of achieving exact recovery of the complex reflectivity at subwavelength resolution. Additionally, this resolution analysis shows that noise in the data effectively acts as a regularization to the imaging functional resulting in a method that is surprisingly more robust and effective with noise than without noise.
more » « less- Award ID(s):
- 1840265
- NSF-PAR ID:
- 10302824
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Inverse Problems
- Volume:
- 37
- Issue:
- 12
- ISSN:
- 0266-5611
- Page Range / eLocation ID:
- Article No. 125006
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract While land-based high-frequency (HF) radars are the only instruments capable of resolving both the temporal and spatial variability of surface currents in the coastal ocean, recent high-resolution views suggest that the coastal ocean is more complex than presently deployed radar systems are able to reveal. This work uses a hybrid system, having elements of both phased arrays and direction finding radars, to improve the azimuthal resolution of HF radars. Data from two radars deployed along the U.S. East Coast and configured as 8-antenna grid arrays were used to evaluate potential direction finding and signal, or emitter, detection methods. Direction finding methods such as maximum likelihood estimation generally performed better than the well-known multiple signal classification (MUSIC) method given identical emitter detection methods. However, accurately estimating the number of emitters present in HF radar observations is a challenge. As MUSIC’s direction-of-arrival (DOA) function permits simple empirical tests that dramatically aid the detection process, MUSIC was found to be the superior method in this study. The 8-antenna arrays were able to provide more accurate estimates of MUSIC’s noise subspace than typical 3-antenna systems, eliminating the need for a series of empirical parameters to control MUSIC’s performance. Code developed for this research has been made available in an online repository.
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Purpose To develop an improved k‐space reconstruction method using scan‐specific deep learning that is trained on autocalibration signal (ACS) data.
Theory Robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction trains convolutional neural networks on ACS data. This enables nonlinear estimation of missing k‐space lines from acquired k‐space data with improved noise resilience, as opposed to conventional linear k‐space interpolation‐based methods, such as GRAPPA, which are based on linear convolutional kernels.
Methods The training algorithm is implemented using a mean square error loss function over the target points in the ACS region, using a gradient descent algorithm. The neural network contains 3 layers of convolutional operators, with 2 of these including nonlinear activation functions. The noise performance and reconstruction quality of the RAKI method was compared with GRAPPA in phantom, as well as in neurological and cardiac in vivo data sets.
Results Phantom imaging shows that the proposed RAKI method outperforms GRAPPA at high (≥4) acceleration rates, both visually and quantitatively. Quantitative cardiac imaging shows improved noise resilience at high acceleration rates (rate 4:23% and rate 5:48%) over GRAPPA. The same trend of improved noise resilience is also observed in high‐resolution brain imaging at high acceleration rates.
Conclusion The RAKI method offers a training database‐free deep learning approach for MRI reconstruction, with the potential to improve many existing reconstruction approaches, and is compatible with conventional data acquisition protocols.
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ABSTRACT The quasar target selection for the upcoming survey of the Dark Energy Spectroscopic Instrument (DESI) will be fixed for the next 5 yr. The aim of this work is to validate the quasar selection by studying the impact of imaging systematics as well as stellar and galactic contaminants, and to develop a procedure to mitigate them. Density fluctuations of quasar targets are found to be related to photometric properties such as seeing and depth of the Data Release 9 of the DESI Legacy Imaging Surveys. To model this complex relation, we explore machine learning algorithms (random forest and multilayer perceptron) as an alternative to the standard linear regression. Splitting the footprint of the Legacy Imaging Surveys into three regions according to photometric properties, we perform an independent analysis in each region, validating our method using extended Baryon Oscillation Spectroscopic Survey (eBOSS) EZ-mocks. The mitigation procedure is tested by comparing the angular correlation of the corrected target selection on each photometric region to the angular correlation function obtained using quasars from the Sloan Digital Sky Survey (SDSS) Data Release 16. With our procedure, we recover a similar level of correlation between DESI quasar targets and SDSS quasars in two-thirds of the total footprint and we show that the excess of correlation in the remaining area is due to a stellar contamination that should be removed with DESI spectroscopic data. We derive the Limber parameters in our three imaging regions and compare them to previous measurements from SDSS and the 2dF QSO Redshift Survey.
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Single-molecule super-resolution imaging is instrumental in investigating cellular architecture and organization at the nanoscale. Achieving precise 3D nanometric localization when imaging structures throughout mammalian cells, which can be multiple microns thick, requires careful selection of the illumination scheme in order to optimize the fluorescence signal to background ratio (SBR). Thus, an optical platform that combines different wide-field illumination schemes for target-specific SBR optimization would facilitate more precise 3D nanoscale studies of a wide range of cellular structures. Here, we demonstrate a versatile multimodal illumination platform that integrates the sectioning and background reduction capabilities of light sheet illumination with homogeneous, flat-field epi- and TIRF illumination. Using primarily commercially available parts, we combine the fast and convenient switching between illumination modalities with point spread function engineering to enable 3D single-molecule super-resolution imaging throughout mammalian cells. For targets directly at the coverslip, the homogenous intensity profile and excellent sectioning of our flat-field TIRF illumination scheme improves single-molecule data quality by providing low fluorescence background and uniform fluorophore blinking kinetics, fluorescence signal, and localization precision across the entire field of view. The increased contrast achieved with LS illumination, when compared with epi-illumination, makes this illumination modality an excellent alternative when imaging targets that extend throughout the cell. We validate our microscopy platform for improved 3D super-resolution imaging by two-color imaging of paxillin – a protein located in the focal adhesion complex – and actin in human osteosarcoma cells.
