Spatial versus spectral compression ratio in compressive sensing of hyperspectral imaging

August, Yitzhak; Vachman, Chaim; Stern, Adrian

doi:10.1117/12.2017949

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…For a pixel that does not include the target, the MF takes the form of Equation (13). However, when the target is present we use an additive, as shown in Equation (15), and the MF takes the form of Equation (16):…”

Section: Target Detectionmentioning

confidence: 99%

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Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Oiknine¹,

August²,

Farber³

et al. 2018

J. Imaging

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Hyperspectral (HS) imaging involves the sensing of a scene’s spectral properties, which are often redundant in nature. The redundancy of the information motivates our quest to implement Compressive Sensing (CS) theory for HS imaging. This article provides a review of the Compressive Sensing Miniature Ultra-Spectral Imaging (CS-MUSI) camera, its evolution, and its different applications. The CS-MUSI camera was designed within the CS framework and uses a liquid crystal (LC) phase retarder in order to modulate the spectral domain. The outstanding advantage of the CS-MUSI camera is that the entire HS image is captured from an order of magnitude fewer measurements of the sensor array, compared to conventional HS imaging methods.

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Section: Target Detectionmentioning

confidence: 99%

“…Studies have shown that the huge HS datacubes are often highly redundant [13][14][15][16][17] and, therefore, very compressible or sparse. This gives the incentive to implement Compressive Sensing (CS) theory in HS systems.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Oiknine¹,

August²,

Farber³

et al. 2018

J. Imaging

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“…As a result, the acquisition time is the same as that with the single pixel camera. The separable encoding has significant benefits in terms of computational efforts [6,14].…”

Section: Single Pixel Compressive Camera With Spectral Encodingmentioning

confidence: 99%

Hyperspectral compressive imaging

Stern

August

Farber

et al. 2013

2013 12th Workshop on Information Optics (WIO)

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Hyperspectral imaging is a natural field for the implementation of compressive sensing because typical captured hyperspectral datacubes involve large amount of data which is also often very redundant. Compressive hypersepctral systems compress the hyperspectral data already in the acquisition step, thus remedy the severe storage and processing requirements typical to conventional hypersepctral imaging systems. In this paper we overview some hyperspectral compressive imaging techniques that we have developed.Index Terms-compressed sensing, compressive sensing, compressive imaging, computational imaging.978-1-4799-0768-7/13/$31.00 c 2013 IEEE

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“…transform domain, such as wavelet or Fourier domain [8], and the spectral information of the object points shows significant redundancy [9], the datacube of the target can be represented by a few elements from a basis or dictionary, which makes it possible to obtain the entire datacube in a single snapshot by a format sensor [10]. The coded aperture snapshot spectral imager (CASSI) proposed by Duke University employs a coded aperture to encode the input image in spatial domain and disperse the coded image by a prism to capture the mixture spatio-spectral data on a detector, and the entire 3-D datacube is recovered by CS algorithm [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

The Datacube Reconstruction Approach for Compressed Sensing Image Mapping Spectrometer (CSIMS)

Ding

Wang

2019

IEEE Access

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Compressed sensing image mapping spectrometer can acquire the entire three-dimension (3-D) datacube of the objects instantaneously in a snapshot. The system can slice and encode the input image to different parts and disperses each part to be imaged on the detector. The reconstruction algorithm utilizes the sparse of the datacube in spatial and spectral dimensions to estimate the optimal results by compressed sensing concept. This paper establishes the sensing matrix for a novel datacube reconstruction approach, which combines the whole sliced parts to make full use of the raw mixture data. The simulations are conducted under different values of system parameters. The results show that the more pieces is sliced from the input image, the quality of the reconstructed datacube is higher, which reveals that the reconstruction approach proposed in this paper is effective to improve the quality of the datacube.INDEX TERMS Snapshot imaging spectrometer, compressed sensing, datacube, reconstruction.

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Spatial versus spectral compression ratio in compressive sensing of hyperspectral imaging

Cited by 8 publications

References 30 publications

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Hyperspectral compressive imaging

The Datacube Reconstruction Approach for Compressed Sensing Image Mapping Spectrometer (CSIMS)

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