Fast Hyperspectral Cube Reconstruction for a Double Disperser Imager

Ardi, Ibrahim; Carfantan, Hervé; Lacroix, Simon; Monmayrant, Antoine

doi:10.23919/eusipco.2018.8553615

Cited by 5 publications

(9 citation statements)

References 8 publications

(11 reference statements)

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“…We follow the work of Ardi et al [16], who proposed a convex relaxation algorithm to recover the HS data cube. Although the DD CASSI is a snapshot imager, Ardi et al [16] suggested performing the cube recovery based on multiple acquisitions. This improves the reconstruction quality (see Section 5.2.2).…”

Section: Dd Cassi Matrix Modelmentioning

confidence: 99%

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Feasibility of a Real-Time Embedded Hyperspectral Compressive Sensing Imaging System

Lim

Mancini

Mura

2022

Sensors

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Hyperspectral imaging has been attracting considerable interest as it provides spectrally rich acquisitions useful in several applications, such as remote sensing, agriculture, astronomy, geology and medicine. Hyperspectral devices based on compressive acquisitions have appeared recently as an alternative to conventional hyperspectral imaging systems and allow for data-sampling with fewer acquisitions than classical imaging techniques, even under the Nyquist rate. However, compressive hyperspectral imaging requires a reconstruction algorithm in order to recover all the data from the raw compressed acquisition. The reconstruction process is one of the limiting factors for the spread of these devices, as it is generally time-consuming and comes with a high computational burden. Algorithmic and material acceleration with embedded and parallel architectures (e.g., GPUs and FPGAs) can considerably speed up image reconstruction, making hyperspectral compressive systems suitable for real-time applications. This paper provides an in-depth analysis of the required performance in terms of computing power, data memory and bandwidth considering a compressive hyperspectral imaging system and a state-of-the-art reconstruction algorithm as an example. The results of the analysis show that real-time application is possible by combining several approaches, namely, exploitation of system matrix sparsity and bandwidth reduction by appropriately tuning data value encoding.

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Section: Dd Cassi Matrix Modelmentioning

confidence: 99%

“…For this purpose, we perform an analysis based on "fast hyperspectral cube reconstruction" technology proposed by Ardi et al [16]. They exploit the DD CASSI and process the data with the Conjugate Gradient for Normal Equation (CGNE), a convex relaxation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of a Real-Time Embedded Hyperspectral Compressive Sensing Imaging System

Lim

Mancini

Mura

2022

Sensors

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“…To prevent smoothing across an edge, the components of the matrices Dx and Dy corresponding to an edge pixel are set to zero [25]. The edges are detected on the panchromatic image using a state-of-the-art edge finding procedure.…”

Section: Reconstruction Of the Hyperspectral Datacube Via Edge Presermentioning

confidence: 99%

“…The used reconstruction algorithm is based on an edgepreserving regularization which smoothes spectral-spatial features, which obliges nearby pixels to have similar spectra but avoids smoothing of sharp spatial features, therefore preventing mixing of the spectra between distinct regions [25]. This regularization is related to edge-preserving regularization [26] and to the well-known Total Variation [27] regularization which aims to preserve the unknown edges, while smoothing the images to regularize the solution.…”

Section: Introductionmentioning

confidence: 99%

Optimized coded aperture for frugal hyperspectral image recovery using a dual-disperser system

et al. 2020

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We present a novel acquisition scheme based on a dual disperser architecture, which can reconstruct a hyperspectral data cube using many times fewer acquisitions than spectral bands. The reconstruction algorithm follows a quadratic regularization approach, based on the assumption that adjacent pixels in the scene share similar spectra, and if they do not, this corresponds to an edge which is detectable on the panchromatic image. A digital micro-mirror device (DMD) applies reconfigurable spectral-spatial filtering to the scene for each acquisition, and the filtering code is optimized considering the physical properties of the system. The algorithm is tested on simple multi-spectral scenes with 110 wavelength bands, and is able to accurately reconstruct the hyperspectral datacube using only 10 acquisitions.

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“…Classification of multiple spectra is possible with only a few acquisitions as shown in [18,19], using a scheme where the DMD mask adapts for each new acquisition to improve classification accuracy. Furthermore, there is potential to use regularization approaches to exploit homogeneity within the hyperspectral datacube, allowing full cube reconstruction with only a handful of acquisitions [20]. The easy access to the panchromatic image allows an adaptive approach to datacube acquisition, whereby one exploits features from the panchromatic image to optimize the DMD mask for a particular acquisition scheme, or even change the acquisition scheme itself.…”

Section: Introductionmentioning

confidence: 99%

Calibration of programmable spectral imager with dual disperser architecture

Hemsley

Lacroix

Carfantan

et al. 2020

Optics Communications

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We present a hyperspectral imager with a dual disperser architecture, which is highly re-configurable thanks to the use of a digital micro-mirror device (DMD). The system has over 200 spectral bands in the range 400-700 nm, and 2048 by 2048 spatial pixels. We describe a method to calibrate the instrument and quantify its performance using a simple slit scanning acquisition scheme, and discuss various methods to remove artifacts in the spectral datacube via postprocessing. This prototype instrument is designed for simplicity, and aims to explore new acquisition schemes that exploit the property of colocation and use the programmable nature of the DMD to increase the speed of datacube retrieval and the quality of the hyperspectral data.

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Fast Hyperspectral Cube Reconstruction for a Double Disperser Imager

Cited by 5 publications

References 8 publications

Feasibility of a Real-Time Embedded Hyperspectral Compressive Sensing Imaging System

Feasibility of a Real-Time Embedded Hyperspectral Compressive Sensing Imaging System

Optimized coded aperture for frugal hyperspectral image recovery using a dual-disperser system

Calibration of programmable spectral imager with dual disperser architecture

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