Dual-ARM VIS/NIR compressive spectral imager

Rueda, Hoover; Argüello, Henry; Arce, Gonzalo R.

doi:10.1109/icip.2015.7351267

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…Notice that the previous methods apply a reordering stage that considers the information of the coded aperture patterns. In this work, the information embedded in coded aperture patterns is included in the projection matrices (9) and (14) leading to an algorithm that fuses spectral image features directly from compressive measurements.…”

Section: Pavia Universitymentioning

confidence: 99%

“…Multiple CASSI based architectures have been proposed such as the three-dimensional CASSI (3D-CASSI) [5], the colored CASSI [7], and the snapshot colored compressive spectral imager (SCCSI) [8]. Moreover, various architectures that combine different kinds of measurements have been recently proposed to recover high-resolution spectral images [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

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Multiresolution Compressive Feature Fusion for Spectral Image Classification

Ramirez

Argüello

2019

IEEE Trans. Geosci. Remote Sensing

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In the compressive spectral imaging (CSI) framework, different architectures have been proposed to recover high-resolution spectral images from compressive measurements. Since CSI architectures compactly capture the relevant information of the spectral image, various methods that extract classification features from compressive samples have been recently proposed. However, these techniques require a feature extraction procedure that reorders measurements using the information embedded in the coded aperture patterns. In this paper, a method that fuses features directly from multiresolution compressive measurements is proposed for spectral image classification. More precisely, the fusion method is formulated as an inverse problem that estimates high-spatial-resolution and low-dimensional feature bands from compressive measurements. To this end, the decimation matrices that describe the compressive measurements as degraded versions of the fused features are mathematically modeled using the information embedded in the coded aperture patterns. Furthermore, we include both a sparsity-promoting and a total-variation (TV) regularization terms to the fusion problem in order to consider the correlations between neighbor pixels, and therefore, improve the accuracy of pixel-based classifiers. To solve the fusion problem, we describe an algorithm based on the accelerated variant of the alternating direction method of multipliers (accelerated-ADMM). Additionally, a classification approach that includes the developed fusion method and a multilayer neural network is introduced. Finally, the proposed approach is evaluated on three remote sensing spectral images and a set of compressive measurements captured in the laboratory. Extensive simulations show that the proposed classification approach outperforms other approaches under various performance metrics.

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Section: Pavia Universitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiresolution Compressive Feature Fusion for Spectral Image Classification

Ramirez

Argüello

2019

IEEE Trans. Geosci. Remote Sensing

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“…CASSI's multishot variant improves spectral recovery, though it comes at the sacrifice of the acquisition speed [20]. A more recent treatment of CASSI was reported in [21].…”

Section: Fourier Transform Spectroscopymentioning

confidence: 99%

Fourier multispectral imaging

Jia¹,

Ni²,

Sarangan³

et al. 2015

Opt. Express

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Current multispectral imaging systems use narrowband filters to capture the spectral content of a scene, which necessitates different filters to be designed for each application. In this paper, we demonstrate the concept of Fourier multispectral imaging which uses filters with sinusoidally varying transmittance. We designed and built these filters employing a single-cavity resonance, and made spectral measurements with a multispectral LED array. The measurements show that spectral features such as transmission and absorption peaks are preserved with this technique, which makes it a versatile technique than narrowband filters for a wide range of multispectral imaging applications.

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“…Another promising way to boost the performance of CASSI is to capture a non-diffracted image which aids in the reconstruction. However, this approach normally requires splitting an incoming beam and employing two cameras [19][20][21][22][23], which makes the CASSI system inconveniently complex and causes a loss in the light intensity, which can reach as much as 50% [23].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of CASSI by a zero-order image employing a single detector

et al. 2021

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Coded aperture snapshot spectral imaging (CASSI) makes it possible to recover 3D hyperspectral data from a single 2D image. However, the reconstruction problem is severely underdetermined, and efforts to improve the compression ratio typically make the imaging system more complex and cause a significant loss of incoming light intensity. In this paper, we propose a novel approach to CASSI that enables capturing both a spectrally sheared and an integrated image of a scene with a single camera. We performed hyperspectral imaging of three different testing scenes in the spectral range of 500–900 nm. We demonstrate the prominent effect of using the nondiffracted image on the reconstruction of data from our camera. The use of the spectrally integrated image improves the reconstruction quality, and we observed an approximately fivefold reduction in reconstruction time.

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Dual-ARM VIS/NIR compressive spectral imager

Cited by 8 publications

References 18 publications

Multiresolution Compressive Feature Fusion for Spectral Image Classification

Multiresolution Compressive Feature Fusion for Spectral Image Classification

Fourier multispectral imaging

Enhancement of CASSI by a zero-order image employing a single detector

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