Compressive Coded Aperture Spectral Imaging: An Introduction

Arce, Gonzalo R.; Brady, David J.; Carin, Lawrence; Argüello, Henry; Kittle, David S.

doi:10.1109/msp.2013.2278763

Cited by 548 publications

(271 citation statements)

References 30 publications

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“…This new technique involves diverse mathematical areas such as numerical optimization, signal processing, random matrix analysis, and statistics. The enormous potential of CS has been recently applied in areas such as microscopy, holography, tomography and spectroscopy (Arce et al, 2014;Brady et al, 2009;Studer et al, 2012;Wagadarikar et al, 2008;Yu and Wang, 2009). CS allows sensing a signal with a fewer number of samples than that required by the Nyquist criterion.…”

Section: Introductionmentioning

confidence: 99%

Probability of correct reconstruction in compressive spectral imaging

2016

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Coded Aperture Snapshot Spectral Imaging (CASSI) systems capture the 3-dimensional (3D) spatio-spectral information of a scene using a set of 2-dimensional (2D) random coded Focal Plane Array (FPA) measurements. A compressed sensing reconstruction algorithm is then used to recover the underlying spatio-spectral 3D data cube. The quality of the reconstructed spectral images depends exclusively on the CASSI sensing matrix, which is determined by the statistical structure of the coded apertures. The Restricted Isometry Property (RIP) of the CASSI sensing matrix is used to determine the probability of correct image reconstruction and provides guidelines for the minimum number of FPA measurement shots needed for image reconstruction. Further, the RIP can be used to determine the optimal structure of the coded projections in CASSI. This article describes the CASSI optical architecture and develops the RIP for the sensing matrix in this system. Simulations show the higher quality of spectral image reconstructions when the RIP property is satisfied. Simulations also illustrate the higher performance of the optimal structured projections in CASSI.Keywords: Restricted Isometry Property, RIP, CASSI, compressive sensing, spectral imaging, coded aperture. RESUMENEl sistema de adquisición de imágenes espectrales de única captura basado en apertura codificada (CASSI), capta información tridimensional (3D) espacio-espectral de una escena, usando un conjunto de medidas bidimensionales (2D) proyectadas en un FPA (Focal Plane Array). Para recuperar el cubo de datos a partir de las proyecciones en el FPA, se usa un algoritmo de reconstrucción basado en la teoría de muestreo compresivo. En CASSI la calidad de la reconstrucción de imágenes espectrales depende exclusivamente de la matriz de sensado, que es determinada por la estructura estadística del código de apertura. La propiedad restringida isométrica (RIP) de la matriz de sensado CASSI es usada para determinar la probabilidad de una correcta reconstrucción de la imagen. Este artículo describe la arquitectura óptima CASSI y desarrolla la RIP para las matrices de muestreo, para la captura de la información del cubo de datos. En efecto, la RIP provee la guía para determinar el mínimo número de capturas FPA necesarias para la reconstrucción de una imagen. Más adelante, la RIP es usada para encontrar la estructura óptima de las proyecciones de los códigos de apertura de CASSI. Las simulaciones muestran alta calidad de la reconstrucción obtenida de las imágenes espectrales cuando se satisface la condición impuesta por la RIP. También muestran el más alto rendimiento obtenido de las estructuras óptimas de las proyecciones CASSI.

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Section: Introductionmentioning

confidence: 99%

Probability of correct reconstruction in compressive spectral imaging

2016

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“…In this case, the sampling matrix Φ Φ Φ corresponds to a optical system. For instance, CASSI system [10] is an architecture that attains CSI measurements, in three main steps: first encoding the information with a code aperture pattern, second using a prism as a dispersive element that shifts the spectral information and finally impinging in a focal plane array (FPA) detector.…”

Section: Compressive Sensing and Compressive Spectral Imagingmentioning

confidence: 99%

“…In addition, Compressive Spectral Imaging (CSI) is an interesting CS application where the data of a multispectral image involves a large amount of spatial and spectral information that can be represented with fewer compressive samples; in some cases, the amount of data in CSI can be reduced a 90%. In this field, three of the most remarkable of these CSI architectures are the spatio-spectral encoded compressive HS imager (SSCSI) [9], the coded aperture snapshot spectral imagers (CASSI) [10] and snapshot colored compressive spectral imager (SCCSI) [11].…”

Section: Introductionmentioning

confidence: 99%

Compressive multispectral model for spectrum sensing in cognitive radio networks

Marin

Martinez

Betancur

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Cognitive Radio (CR) is one of the most promising techniques for optimizing the spectrum usage. However, the large amount of data of spectral information that must be processed to identify and assign spectral resources increases the channel assignment times, therefore worsening the quality of service for the devices using the spectrum. Compressive Sensing (CS) is a digital processing technique that allows the reconstruction of sparse or compressible signals using fewer samples than those required traditionally. This paper presents a model that addresses the Spectral Sensing problem in Cognitive Radio using Compressive Sensing as an effective way of decreasing the number of samples required in the sensing process. This model is based on Compressive Spectral Imaging (CSI) architectures where a centralized spectrum manager selects what power data must be delivered by the different wireless devices using binary patterns, and builds a multispectral data cube image with the geographical and spectral data power information. The results show that this multispectral data cube can be built with only a 50% of the samples generated by the devices and, therefore reducing the data traffic dramatically.

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“…Information of the spectral properties of an object permits to know its components, which in turn can be used to detect or classify the elements in a certain image. As a result of the appearance of compressive sensing (CS) [1], new methodologies were proposed to effectively capture spectral images, such as compressive spectral imaging (CSI) [2]. CSI imaging systems are modeled based on the principles of a traditional spectrophotometer which records This research was supported in part by Intel/NSF VEC 1538950.…”

Section: Introductionmentioning

confidence: 99%

High-dimensional optimization of color coded apertures for compressive spectral cameras

Rueda

Argüello

Arce

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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A spectral image can be regarded as a three-dimensional cube where each pixel is a vector of intensities representing a spectral signature. Compressive spectral imaging (CSI) is a sensing and reconstruction framework, based on the fundamentals of the compressive sensing theory, which focuses on capturing spectral images efficiently, exploiting their highly correlated information by coding its spectral characteristics commonly using a black-and-white, grayscale or recently a color coded aperture. The distribution of the entries of the coded apertures determines the quality of the estimated spectral images. State of the art methods have used random coded apertures, and some optimization procedures have focused on the optimal design of horizontal sections of the coded apertures; however, they do not fully exploit the spatio-spectral correlations within the spectral images. To that end, in this paper, it is proposed a high-dimensional optimization procedure to design color coded apertures for CSI systems, which exploits not only the spectral correlations but also the spatial correlations within an spectral image. Simulations analyzing the conditioning of the sensing matrices, as well as the reconstruction quality of the attained spectral images show the improvement entailed by the proposed method.Index Terms-Compressive spectral imaging, coded aperture design, color filter array, numerical optimization.

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Compressive Coded Aperture Spectral Imaging: An Introduction

Cited by 548 publications

References 30 publications

Probability of correct reconstruction in compressive spectral imaging

Probability of correct reconstruction in compressive spectral imaging

Compressive multispectral model for spectrum sensing in cognitive radio networks

High-dimensional optimization of color coded apertures for compressive spectral cameras

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