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Future challenges in Big Imaging problems will require that traditional, "black-box" machine learning methods, be revisited from the perspective of ongoing efforts in distributed computing. This paper proposes a distributed architecture for astrophysical imagery, which exploits the Apache Spark framework for the efficient parallelization of the learning problem at hand. The use case is related...
In this paper, we investigate the recovery of range and spectral profiles associated with remote three-dimensional scenes sensed via single-photon multispectral Lidar (MSL). We consider different spatial/spectral sampling strategies and pare their performance for similar overall numbers of detected photons. For a regular spatial grid, the first strategy consists of sampling all the spatial locations...
This paper proposes a novel way to design seismic migration Finite Impulse Response (FIR) digital filters using the Newton minimization algorithm. The algorithm requires computing the inverse of the Jacobian matrix, which is non-square for the seismic migration filters problem. In this case, we suggest using the Moore-Penrose pseudo-inverse to obtain the inverse of the Jacobian matrix. The proposed...
In this paper we present our end-to-end model of the imaging pipeline in the Square Kilometre Array. Our Sky Generator models the signals that are received by the Central Signal Processor (CSP), our CSP Correlator model then processes those signals to generate visibilities to pass to the Science Data Processor (SDP). Our SDP Imaging model then grids the visibilities and inverse Fourier transforms...
Advances in ultrasound technology have fueled the emergence of Point-Of-Care Ultrasound (PoCU) imaging, including improved ease-of-use, superior image quality, and lower cost ultrasound. One of the approaches that can make the adoption of PoCU universal is to make the data acquisition module as simple as a "stethoscope" while further processing and image construction can be done using cloud-based...
Spectral imaging is useful in a wide range of applications for non-invasive detection and classification. However, the massive amount of involved data increases its processing and storing costs. In contrast, compressive spectral imaging (CSI) establishes that the three-dimensional data cube can be recovered from a small set of projections, that are generally captured in 2-dimensional detectors. Furthermore,...
We propose, under the form of a short overview, to stress the interest of graph to encode the "topological" structure of networks hidden in images especially when applied in life sciences. We point toward existing computer science tools to extract such structural graph from images. We then illustrate different applications, such as segmentation, denoising, and simulation on practical examples...
Near-field ultrawideband imaging is a promising remote sensing technique in various applications such as airport security, surveillance, medical diagnosis, and through-wall imaging. Recently, there has been increasing interest in using sparse multiple-input-multiple-output (MIMO) arrays to reduce hardware complexity and cost. In this paper, based on a Bayesian estimation framework, an optimal design...
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