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      Arquitectura óptica de único pixel para el sensado compresivo de imágenes hiperespectrales Translated title: Single-pixel optical sensing architecture for compressive hyperspectral imaging

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          Abstract

          Los sistemas de sensado de imágenes espectrales (CSI) capturan información tridimensional (3D) de una escena usando mediciones codificadas en dos dimensiones (2D). Estas mediciones son procesadas posteriormente por un algoritmo de optimización para obtener una estimación de la información tridimensional. La calidad de las reconstrucciones obtenidas depende altamente de la resolución del detector, cuyo costo aumenta exponencialmente a mayor resolución exhiba. Así, reconstrucciones de alta resolución son requeridas, pero a bajo costo. Este artículo propone una arquitectura óptica de sensado compresivo que utiliza un único pixel como detector para la captura y reconstrucción de imágenes hiperespectrales. Esta arquitectura óptica depende del uso de múltiples capturas de imágenes procesadas por medio de dos aperturas codificadas que varían en cada toma, y un elemento de dispersión. Diferentes simulaciones con 2 bases de datos distintas muestran resultados promisorios que permiten reconstruir una imagen hiperespectral utilizando tan solo el 30% de los vóxeles de la imagen original.

          Translated abstract

          Compressive hyperspectral imaging systems (CSI) capture the threedimensional (3D) information of a scene by measuring two-dimensional (2D) coded projections in a Focal Plane Array (FPA). These projections are then exploited by means of an optimization algorithm to obtain an estimation of the underlying 3D information. The quality of the reconstructions is highly dependent on the resolution of the FPA detector, which cost grows exponentially with the resolution. High-resolution low-cost reconstructions are thus desirable. This paper proposes a Single Pixel Compressive Hyperspectral Imaging Sensor (SPHIS) to capture and reconstruct hyperspectral images. This optical architecture relies on the use of multiple snapshots of two timevarying coded apertures and a dispersive element. Several simulations with two different databases show promising results as the reliable reconstruction of a hyperspectral image can be achieved by using as few as just the 30% of its voxels.

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          Most cited references 21

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          Compressed sensing

           D.L. Donoho (2006)
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            Single-Pixel Imaging via Compressive Sampling

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              Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems

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                Author and article information

                Journal
                rfiua
                Revista Facultad de Ingeniería Universidad de Antioquia
                Rev.fac.ing.univ. Antioquia
                Facultad de Ingeniería, Universidad de Antioquia (Medellín, Antioquia, Colombia )
                0120-6230
                2422-2844
                December 2014
                : 73
                : 124-133
                Affiliations
                Bucaramanga orgnameUniversidad Industrial de Santander orgdiv1Escuela de Ingeniería de Sistemas e Informática Colombia
                orgnameUniversidad Industrial de Santander rueda@ 123456udel.edu
                Newark orgnameUniversity of Delaware orgdiv1Department of Electrical and Computer Engineering USA
                Article
                S0120-62302014000400013 S0120-6230(14)00007313

                This work is licensed under a Creative Commons Attribution 4.0 International License.

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                Figures: 0, Tables: 0, Equations: 0, References: 21, Pages: 10
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