Code aperture optimization for spectrally agile compressive imaging

Argüello, Henry; Arce, Gonzalo R.

doi:10.1117/12.893590

Cited by 30 publications

(61 citation statements)

References 5 publications

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“…For spectrally rich or spatially detailed images, a single-shot FPA measurement is not sufficient to achieve proper quality reconstructions, and additional shots are required. The CASSI architecture is capable of admitting multiple snapshots, each with a different coded aperture pattern, thus yielding a less ill-posed inverse problem and improved signal reconstructions [18]. The set of k << L FPA measurements is given by y = Hf + ω, where y = y 0…”

Section: Introductionmentioning

confidence: 99%

An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture

Boada

Garcia

Ferreira

et al. 2017

I&U:EfD

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<p>Hyperspectral imaging entails data typically spanning hundreds of contiguous wavebands in a certain spectral range. Each spatial point in hyperspectral images is therefore represented by a vector whose entries correspond to the intensity on each spectral band. These images enable object and feature detection, classification, or identification based on their spectral characteristics. Novel architectures have been developed for the acquisition of compressive spectral images with just a few coded aperture focal plane array measurements. This work focuses on the development of a target detection approach in hyperspectral images directly from compressive measurements without first reconstructing the full data cube that represents the real image. Specifically, a sparsity-based target detection model that uses compressive measurement for the detection task is designed and tested using an optimization algorithm. Simulations show that it is possible to perform certain transformations to the dictionaries used in traditional target detection, in order to achieve an accurate image representation in the compressed subspace</p>

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Section: Introductionmentioning

confidence: 99%

An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture

Boada

Garcia

Ferreira

et al. 2017

I&U:EfD

View full text Add to dashboard Cite

show abstract

“…In this modification, each shot uses a distinct coded aperture that remains fixed during the integration time of the detector. The quality of the reconstructed images improves in CASSI in proportion to the number of compressive measurements (Arguello and Arce, 2011). Each CASSI shot adds simultaneously N(N+L-1) compressive measurements.…”

Section: Introductionmentioning

confidence: 99%

High-dynamic range compressive spectral imaging by grayscale coded aperture adaptive filtering

Díaz¹,

Rueda²,

Argüello³

2015

ing.inv.

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The coded aperture snapshot spectral imaging system (CASSI) is an imaging architecture which senses the three dimensional information of a scene with two dimensional (2D) focal plane array (FPA) coded projection measurements. A reconstruction algorithm takes advantage of the compressive measurements sparsity to recover the underlying 3D data cube. Traditionally, CASSI uses block-unblock coded apertures (BCA) to spatially modulate the light. In CASSI the quality of the reconstructed images depends on the design of these coded apertures and the FPA dynamic range. This work presents a new CASSI architecture based on grayscaled coded apertures (GCA) which reduce the FPA saturation and increase the dynamic range of the reconstructed images. The set of GCA is calculated in a real-time adaptive manner exploiting the information from the FPA compressive measurements. Extensive simulations show the attained improvement in the quality of the reconstructed images when GCA are employed. In addition, a comparison between traditional coded apertures and GCA is realized with respect to noise tolerance. Keywords:Compressive sensing, hyperspectral imaging, saturation, dynamic range, coded aperture, optical imaging. RESUMENEl sistema de adquisición de imágenes espectrales basado en una apertura codificada de única captura (CASSI) es una arquitectura para muestrear imágenes espectrales que captura información de una escena en tres dimensiones mediante medidas proyectadas y codificadas del sensor en dos dimensiones (2D). Para recuperar el cubo de datos se emplea un algoritmo de reconstrucción que aprovecha la esparcidad de las medidas compresivas. Tradicionalmente, CASSI usa aperturas codificadas binarias para modular espacialmente la luz. En CASSI la calidad de las imágenes reconstruidas depende del diseño de estas aperturas codificadas y de la saturación del sensor. Este trabajo presenta una nueva arquitectura CASSI basada en aperturas codificadas de escala de grises (ACG), la cual reduce la saturación en el sensor e incrementa el rango dinámico de las imágenes reconstruidas. El conjunto de ACG se calcula en tiempo real de forma adaptativa explotando la información contenida en las medidas compresivas del sensor. La mejora obtenida en la calidad de las imágenes reconstruidas se muestra a partir de diversas simulaciones en las que se utilizan las ACG. Adicionalmente, se lleva a cabo una comparación entre las aperturas codificadas tradicionales y las ACG con respecto a la tolerancia del ruido.

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“…Formally, suppose that a hyperspectral signal ∈ Թ ேൈெൈ , or its vector representation ∈ Թ ேெ , is ܵ-sparse on some basis શ, such that ൌ શી can be approximated by a linear combination of ܵ vectors of શ with ܵ ≪ ‫.ܮܯܰ‬ Here, ܰ ൈ ‫ܯ‬ represents the spatial dimensions, and ‫ܮ‬ is the spectral depth of the image cube. CSI allows to be recovered from ݉ random projections with high probability when ݉ ‫‪ሻ‬ܮܯܰ‪݃ሺ‬݈ܵ‬ ≪ ‫.ܮܯܰ‬ The Coded Aperture Snapshot Spectral Imaging (CASSI) system (Wagadarikar, John, Willett, and Brady, 2008;Arguello and Arce, 2011) is a remarkable imaging architecture that effectively implements CSI. Thus, CASSI senses the 3D spectral information of a scene by using 2D random projections, as depicted in Fig.…”

Section: Introduction 1234mentioning

confidence: 99%

Spectral resolution enhancement of hyperspectral imagery by a multiple-aperture compressive optical imaging system

Rueda¹,

Parada²,

Argüello³

2014

ing.inv.

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The Coded Aperture Snapshot Spectral Imaging (CASSI) system captures the three-dimensional (3D) spatio-spectral information of a scene using a set of two-dimensional (2D) random-coded Focal Plane Array (FPA) measurements. A compressive 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 structure of a set of random coded apertures. In this paper, the CASSI system is generalized by developing a multiple-aperture optical imaging system such that spectral resolution enhancement is attainable. In the proposed system, a pair of high-resolution coded apertures is introduced into the CASSI system, allowing it to encode both spatial and spectral characteristics of the hyperspectral image. This approach allows the reconstruction of superresolved hyperspectral data cubes, where the number of spectral bands is significantly increased and the quality in the spatial domain is greatly improved. Extensively simulated experiments show a gain in the peak-signal-to-noise ratio (PSNR), along with a better fit of the reconstructed spectral signatures to the original spectral data.Keywords: Hyperspectral imaging, Spectral resolution enhancement, Compressive sensing, Coded aperture. RESUMENEl sistema de sensado de imágenes espectrales, basado en la apertura codificada y de única toma (CASSI), captura la información espacial y espectral de una escena; mediante mediciones codificadas aleatorias capturadas en un sensor 2D. Un algoritmo basado en la teoría de sensado compresivo (CS), es utilizado para recuperar la escena tridimensional original a partir de las mediciones aleatorias capturadas. La calidad de reconstrucción de la escena depende exclusivamente, de la matriz de sensado del CASSI, la cual es determinada por la estructura de las aperturas codificadas que son utilizadas.En este artículo, se propone una generalización del sistema CASSI por medio del desarrollo de un sistema óptico multi-apertura, que permite el mejoramiento de la resolución espectral. En el sistema propuesto, un par de aperturas codificadas de alta resolución es introducido en el sistema CASSI, permitiendo así, la codificación tanto espacial como espectral de la imagen hiperespectral. Este enfoque permite la reconstrucción de cubos de datos hiperespectrales, donde el número de las bandas espectrales se aumenta significativamente respecto al original, y la calidad espacial es mejorada en gran medida. Así mismo, los experimentos simulados muestran mejoramiento en la relación de pico-de-señal-a-ruido (PSNR), junto con un mejor ajuste en las firmas espectrales reconstruidas sobre los datos espectrales originales.Palabras clave: imágenes hiperespectrales, mejora de resolución espectral, sensado compresivo y apertura codificada.

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Code aperture optimization for spectrally agile compressive imaging

Cited by 30 publications

References 5 publications

An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture

An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture

High-dynamic range compressive spectral imaging by grayscale coded aperture adaptive filtering

Spectral resolution enhancement of hyperspectral imagery by a multiple-aperture compressive optical imaging system

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