Dual-camera snapshot spectral imaging with a pupil-domain optical diffuser and compressed sensing algorithms

Hauser, Jonathan; Golub, Michael A.; Averbuch, Amir; Nathan, M.; Zheludev, Valery A.; Kagan, Michael

doi:10.1364/ao.380256

Cited by 12 publications

(2 citation statements)

References 29 publications

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“…Other methods use a diffuser (as opposed to a prism) as the dispersive element [30]. This can be more compact than prism-based systems and can have improved spatial resolution when combined with an additional RGB camera [31]. To further improve compactness, [16] uses a single diffractive optic as both the lens and the dispersive element, uniquely encoding spectral information in a spectrally-rotating point spread function (PSF).…”

Section: Related Work a Snapshot Hyperspectral Imagingmentioning

confidence: 99%

Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array

Monakhova

Yanny

Aggarwal

et al. 2020

Optica

150

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Hyperspectral imaging is useful for applications ranging from medical diagnostics to crop monitoring; however, traditional scanning hyperspectral imagers are prohibitively slow and expensive for widespread adoption. Snapshot techniques exist but are often confined to bulky benchtop setups or have low spatio-spectral resolution. In this paper, we propose a novel, compact, and inexpensive computational camera for snapshot hyperspectral imaging. Our system consists of a repeated spectral filter array placed directly on the image sensor and a diffuser placed close to the sensor. Each point in the world maps to a unique pseudorandom pattern on the spectral filter array, which encodes multiplexed spatio-spectral information. A sparsity-constrained inverse problem solver then recovers the hyperspectral volume with good spatio-spectral resolution. By using a spectral filter array, our hyperspectral imaging framework is flexible and can be designed with contiguous or non-contiguous spectral filters that can be chosen for a given application. We provide theory for system design, demonstrate a prototype device, and present experimental results with high spatio-spectral resolution.

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Section: Related Work a Snapshot Hyperspectral Imagingmentioning

confidence: 99%

Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array

Monakhova

Yanny

Aggarwal

et al. 2020

Optica

150

View full text Add to dashboard Cite

show abstract

“…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%

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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Feature fusion via dual-resolution compressive measurement matrix analysis for spectral image classification

Ramirez

Torre

Argüello

2021

Signal Processing: Image Communication

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Dual-camera snapshot spectral imaging with a pupil-domain optical diffuser and compressed sensing algorithms

Cited by 12 publications

References 29 publications

Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array

Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array

Multiresolution Compressive Feature Fusion for Spectral Image Classification

Feature fusion via dual-resolution compressive measurement matrix analysis for spectral image classification

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