7,616 research outputs found

    Quantitative Chemically-Specific Coherent Diffractive Imaging of Buried Interfaces using a Tabletop EUV Nanoscope

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    Characterizing buried layers and interfaces is critical for a host of applications in nanoscience and nano-manufacturing. Here we demonstrate non-invasive, non-destructive imaging of buried interfaces using a tabletop, extreme ultraviolet (EUV), coherent diffractive imaging (CDI) nanoscope. Copper nanostructures inlaid in SiO2 are coated with 100 nm of aluminum, which is opaque to visible light and thick enough that neither optical microscopy nor atomic force microscopy can image the buried interfaces. Short wavelength (29 nm) high harmonic light can penetrate the aluminum layer, yielding high-contrast images of the buried structures. Moreover, differences in the absolute reflectivity of the interfaces before and after coating reveal the formation of interstitial diffusion and oxidation layers at the Al-Cu and Al-SiO2 boundaries. Finally, we show that EUV CDI provides a unique capability for quantitative, chemically-specific imaging of buried structures, and the material evolution that occurs at these buried interfaces, compared with all other approaches.Comment: 12 pages, 8 figure

    Revealing hidden scenes by photon-efficient occlusion-based opportunistic active imaging

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    The ability to see around corners, i.e., recover details of a hidden scene from its reflections in the surrounding environment, is of considerable interest in a wide range of applications. However, the diffuse nature of light reflected from typical surfaces leads to mixing of spatial information in the collected light, precluding useful scene reconstruction. Here, we employ a computational imaging technique that opportunistically exploits the presence of occluding objects, which obstruct probe-light propagation in the hidden scene, to undo the mixing and greatly improve scene recovery. Importantly, our technique obviates the need for the ultrafast time-of-flight measurements employed by most previous approaches to hidden-scene imaging. Moreover, it does so in a photon-efficient manner based on an accurate forward model and a computational algorithm that, together, respect the physics of three-bounce light propagation and single-photon detection. Using our methodology, we demonstrate reconstruction of hidden-surface reflectivity patterns in a meter-scale environment from non-time-resolved measurements. Ultimately, our technique represents an instance of a rich and promising new imaging modality with important potential implications for imaging science.Comment: Related theory in arXiv:1711.0629

    Photon-Efficient Computational 3D and Reflectivity Imaging with Single-Photon Detectors

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    Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with single-photon detectors, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We develop a robust method for estimating depth and reflectivity using on the order of 1 detected photon per pixel averaged over the scene. Our computational imager combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3D structure. Experiments conducted in the presence of strong background light demonstrate that our computational imager is able to accurately recover scene depth and reflectivity, while traditional maximum-likelihood based imaging methods lead to estimates that are highly noisy. Our framework increases photon efficiency 100-fold over traditional processing and also improves, somewhat, upon first-photon imaging under a total acquisition time constraint in raster-scanned operation. Thus our new imager will be useful for rapid, low-power, and noise-tolerant active optical imaging, and its fixed dwell time will facilitate parallelization through use of a detector array.Comment: 11 pages, 8 figure

    Adaptive reconstruction of radar reflectivity in clutter-contaminated areas by accounting for the space-time variability

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    Identification and elimination of clutter is necessary for ensuring data quality in radar Quantitative Precipitation Estimates (QPE). For uncorrected scanning reflectivity after signal processing, the removed areas have been often reconstructed by horizontal interpolation, extrapolation of non-contaminated PPIs aloft, or combining both based on a classification of the precipitation type. We present a general reconstruction method based on the interpolation of clutter-free observations. The method adapts to the type of precipitation by considering the spatial and temporal variability of the field provided by the multi-dimensional semivariogram. Six different formulations have been tested to analyze the gain introduced by each source of information: (1) horizontal interpolation, (2) vertical extrapolation, (3) extrapolation of past observations, (4) volumetric reconstruction, (5) horizontal and temporal reconstruction, and (6) volumetric and temporal reconstruction. The evaluation of the reconstructed fields obtained with the 6 formulations has been done (i) over clutter-free areas by comparison with the originally observed values, and (ii) over the real clutter-contaminated areas by comparison with the rainfall accumulations from a raingauge network. The results for 24 analyzed events (with a variety of convective and widespread cases) suggest that the contribution of extrapolation of past observations is not fundamental, and that the volumetric reconstruction is the one that overall adapted the best to the different situations.Peer ReviewedPostprint (author’s final draft

    A liquid Xenon Positron Emission Tomograph for small animal imaging : first experimental results of a prototype cell

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    A detector using liquid Xenon (LXe) in the scintillation mode is studied for Positron Emission Tomography (PET) of small animals. Its specific design aims at taking full advantage of the Liquid Xenon scintillation properties. This paper reports on energy, time and spatial resolution capabilities of the first LXe prototype module equipped with a Position Sensitive Photo- Multiplier tube (PSPMT) operating in the VUV range (178 nm) and at 165 K. The experimental results show that such a LXe PET configuration might be a promising solution insensitive to any parallax effect.Comment: 34 pages, 18 pages, to appear in NIM

    Computational multi-depth single-photon imaging

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    We present an imaging framework that is able to accurately reconstruct multiple depths at individual pixels from single-photon observations. Our active imaging method models the single-photon detection statistics from multiple reflectors within a pixel, and it also exploits the fact that a multi-depth profile at each pixel can be expressed as a sparse signal. We interpret the multi-depth reconstruction problem as a sparse deconvolution problem using single-photon observations, create a convex problem through discretization and relaxation, and use a modified iterative shrinkage-thresholding algorithm to efficiently solve for the optimal multi-depth solution. We experimentally demonstrate that the proposed framework is able to accurately reconstruct the depth features of an object that is behind a partially-reflecting scatterer and 4 m away from the imager with root mean-square error of 11 cm, using only 19 signal photon detections per pixel in the presence of moderate background light. In terms of root mean-square error, this is a factor of 4.2 improvement over the conventional method of Gaussian-mixture fitting for multi-depth recovery.This material is based upon work supported in part by a Samsung Scholarship, the US National Science Foundation under Grant No. 1422034, and the MIT Lincoln Laboratory Advanced Concepts Committee. We thank Dheera Venkatraman for his assistance with the experiments. (Samsung Scholarship; 1422034 - US National Science Foundation; MIT Lincoln Laboratory Advanced Concepts Committee)Accepted manuscrip
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