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1. Aperture‐Synthesis Radar Imaging With Compressive Sensing for Ionospheric Research

   https://doi.org/10.1029/2019RS006805  

   Hysell, D. L. ; Sharma, P. ; Urco, M. ; Milla, M. A. ( June 2019 , Radio Science)

   Inverse methods involving compressive sensing are tested in the application of two‐dimensional aperture‐synthesis imaging of radar backscatter from field‐aligned plasma density irregularities in the ionosphere. We consider basis pursuit denoising, implemented with the fast iterative shrinkage thresholding algorithm, and orthogonal matching pursuit (OMP) with a wavelet basis in the evaluation. These methods are compared with two more conventional optimization methods rooted in entropy maximization (MaxENT) and adaptive beamforming (linearly constrained minimum variance or often “Capon's Method.”) Synthetic data corresponding to an extended ionospheric radar target are considered. We find that MaxENT outperforms the other methods in terms of its ability to recover imagery of an extended target with broad dynamic range. Fast iterative shrinkage thresholding algorithm performs reasonably well but does not reproduce the full dynamic range of the target. It is also the most computationally expensive of the methods tested. OMP is very fast computationally but prone to a high degree of clutter in this application. We also point out that the formulation of MaxENT used here is very similar to OMP in some respects, the difference being that the former reconstructs the logarithm of the image rather than the image itself from basis vectors extracted from the observation matrix. MaxENT could in that regard be considered a form of compressive sensing.

    

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2. On the Detection of Sharp Upper Mantle Discontinuities with Radon-Transformed Ps Receiver Functions (CRISP-RF)

   Tolulope Olugboji, Ziqi Zhang ( June 2023 , Geophysical journal international)

   Seismic interrogation of the upper mantle from the base of the crust to the top of the mantle transition zone has revealed discontinuities that are variable in space, depth, lateral extent, amplitude and lack a unified explanation for their origin. Improved constraints on the detectability and properties of mantle discontinuities (depth, amplitude, sharpness) can be obtained with Ps receiver functions where energy scatters from P to S as seismic waves propagate across discontinuities of interest. However, due to the interference of crustal multiples, uppermost mantle discontinuities are more commonly imaged with lower-resolution Sp-RFs which are not affected by these multiples. Here, we present a novel method for obtaining ‘Clean Receiver-function Imaging using SParse Radon Filters’ (CRISP-RF). The central idea involves the transformation of Ps-RF-data into a Radon-transformed Ps-RF. This approach results in the decomposition of the signal into its underlying wavefield contributions: direct conversions, multiple reflections, and noise. A selective filter is then applied to create multiple-free, denoised, source-deconvolved seismograms. The Radon transform is implemented using a sparsity-promoting regularization, common in disciplines such as compressive sensing and model-based image reconstruction, e.g., optical and microscopic imaging, magnetic resonance imaging, and radar astronomy. We review different algorithms for solving this optimization problem and, based on synthetic and real data, show that our approach outperforms the conventional Tikhonov-regularized least-squares methods. The application of CRISP-RF to global datasets will advance our understanding of the enigmatic origins of the upper mantle discontinuities like the ubiquitous Mid-Lithospheric Discontinuity (MLD), and the elusive X-discontinuity. 

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3. Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition

   Liu, Jiaming ; Asif, M. Salman ; Wohlberg, Brendt ; Kamilov, Ulugbek ( December 2021 , Conference on Neural Information Processing Systems (NeurIPS))

   The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors. While the empirical imaging performance and the theoretical convergence properties of these algorithms have been widely investigated, their recovery properties have not previously been theoretically analyzed. We address this gap by showing how to establish theoretical recovery guarantees for PnP/RED by assuming that the solution of these methods lies near the fixed-points of a deep neural network. We also present numerical results comparing the recovery performance of PnP/RED in compressive sensing against that of recent compressive sensing algorithms based on generative models. Our numerical results suggest that PnP with a pre-trained artifact removal network provides significantly better results compared to the existing state-of-the-art methods. 

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4. Antarctic Supraglacial Lake Identification Using Landsat-8 Image Classification

   https://doi.org/10.3390/rs12081327  

   Halberstadt, Anna Ruth ; Gleason, Colin J. ; Moussavi, Mahsa S. ; Pope, Allen ; Trusel, Luke D. ; DeConto, Robert M. ( April 2020 , Remote Sensing)

   Surface meltwater generated on ice shelves fringing the Antarctic Ice Sheet can drive ice-shelf collapse, leading to ice sheet mass loss and contributing to global sea level rise. A quantitative assessment of supraglacial lake evolution is required to understand the influence of Antarctic surface meltwater on ice-sheet and ice-shelf stability. Cloud computing platforms have made the required remote sensing analysis computationally trivial, yet a careful evaluation of image processing techniques for pan-Antarctic lake mapping has yet to be performed. This work paves the way for automating lake identification at a continental scale throughout the satellite observational record via a thorough methodological analysis. We deploy a suite of different trained supervised classifiers to map and quantify supraglacial lake areas from multispectral Landsat-8 scenes, using training data generated via manual interpretation of the results from k-means clustering. Best results are obtained using training datasets that comprise spectrally diverse unsupervised clusters from multiple regions and that include rock and cloud shadow classes. We successfully apply our trained supervised classifiers across two ice shelves with different supraglacial lake characteristics above a threshold sun elevation of 20°, achieving classification accuracies of over 90% when compared to manually generated validation datasets. The application of our trained classifiers produces a seasonal pattern of lake evolution. Cloud shadowed areas hinder large-scale application of our classifiers, as in previous work. Our results show that caution is required before deploying ‘off the shelf’ algorithms for lake mapping in Antarctica, and suggest that careful scrutiny of training data and desired output classes is essential for accurate results. Our supervised classification technique provides an alternative and independent method of lake identification to inform the development of a continent-wide supraglacial lake mapping product. 

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5. Coded aperture optimization in compressive X-ray tomography: a gradient descent approach

   Cuadros, Angela ; Arce, Gonzalo ( October 2017 , Optics express)

   Abstract: Coded aperture X-ray computed tomography (CT) has the potential to revolutionize X-ray tomography systems in medical imaging and air and rail transit security - both areas of global importance. It allows either a reduced set of measurements in X-ray CT without degrada- tion in image reconstruction, or measure multiplexed X-rays to simplify the sensing geometry. Measurement reduction is of particular interest in medical imaging to reduce radiation, and airport security often imposes practical constraints leading to limited angle geometries. Coded aperture compressive X-ray CT places a coded aperture pattern in front of the X-ray source in order to obtain patterned projections onto a detector. Compressive sensing (CS) reconstruction algorithms are then used to recover the image. To date, the coded illumination patterns used in conventional CT systems have been random. This paper addresses the code optimization prob- lem for general tomography imaging based on the point spread function (PSF) of the system, which is used as a measure of the sensing matrix quality which connects to the restricted isom- etry property (RIP) and coherence of the sensing matrix. The methods presented are general, simple to use, and can be easily extended to other imaging systems. Simulations are presented where the peak signal to noise ratios (PSNR) of the reconstructed images using optimized coded apertures exhibit significant gain over those attained by random coded apertures. Additionally, results using real X-ray tomography projections are presented. 

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