SASSI — Super-Pixelated Adaptive Spatio-Spectral Imaging

Saragadam, Vishwanath; DeZeeuw, Michael; Baraniuk, Richard G.; Veeraraghavan, Ashok; Sankaranarayanan, Aswin C.

doi:10.1109/tpami.2021.3075228

Cited by 14 publications

(2 citation statements)

References 38 publications

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“…Besides being a multi-frame technique based on a 2D array sensor, the compression ratio in the experiments is less than 10:1. Saragadam et al 46 introduced a video-rate hyperspectral imager by fusing the RGB image of the scene with the spectra sampled from a sparse set of spatial locations, utilizing scene-adaptive spatial sampling. The system allows hyperspectral imaging of 600 × 900 pixels with 30 spectral bands at a frame rate of 18 fps.…”

Section: Introductionmentioning

confidence: 99%

A compressive hyperspectral video imaging system using a single-pixel detector

Xu,

Lu,

Saragadam

et al. 2024

Nat Commun

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Capturing fine spatial, spectral, and temporal information of the scene is highly desirable in many applications. However, recording data of such high dimensionality requires significant transmission bandwidth. Current computational imaging methods can partially address this challenge but are still limited in reducing input data throughput. In this paper, we report a video-rate hyperspectral imager based on a single-pixel photodetector which can achieve high-throughput hyperspectral video recording at a low bandwidth. We leverage the insight that 4-dimensional (4D) hyperspectral videos are considerably more compressible than 2D grayscale images. We propose a joint spatial-spectral capturing scheme encoding the scene into highly compressed measurements and obtaining temporal correlation at the same time. Furthermore, we propose a reconstruction method relying on a signal sparsity model in 4D space and a deep learning reconstruction approach greatly accelerating reconstruction. We demonstrate reconstruction of 128 × 128 hyperspectral images with 64 spectral bands at more than 4 frames per second offering a 900× data throughput compared to conventional imaging, which we believe is a first-of-its kind of a single-pixel-based hyperspectral imager.

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Section: Introductionmentioning

confidence: 99%

A compressive hyperspectral video imaging system using a single-pixel detector

Xu,

Lu,

Saragadam

et al. 2024

Nat Commun

Self Cite

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“…However, the widespread usages of the traditional slit-based hyperspectral imager are still limited by the well-known annoying issues, such as the enormous quantity of the captured data, which makes transmission and storage overloaded, and the contradiction between the system resolution and the signal-to-noise ratio. Fortunately, the compressive hyperspectral imagers (CHIs) [13][14][15][16][17][18][19][20][21] were developed. In a typical CHI system, a three-dimensional hyperspectral scene, i.e., hyperspectral data cube, can be modulated by an encoding mask and then projected onto a two-dimensional detector array to generate a spatio-spectral multiplexed measurement [22,23], which can significantly reduce the data volume and enlarge the light flux; after that, the hyperspectral data cube can be reconstructed via various reconstruction algorithms [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Design and Dispersion Calibration of Direct-Vision Push-Broom Compressive Double-Amici-Prism Hyperspectral Imager

Zhu

et al. 2022

Photonics

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The design and calibration of the dispersive device in a hyperspectral imager significantly affect the performance of hyperspectral imaging, especially the spectral accuracy. To achieve high-accuracy hyperspectral imaging over the visible band, firstly, the geometric and dispersive parameters of the double Amici prism (DAP) that serves as a dispersive device in the direct-vision push-broom compressive hyperspectral imager (PBCHI) are designed and optimized; secondly, a calibration method based on the numerical calculation of the DAP model is put forward, which can turn the conventional pixel-wise dispersive shift calibration by a monochromator into a group of numerical calculations; lastly, a PBCHI prototype is built to test the performances of the designed and calibrated DAP and the hyperspectral imager. The calibration experiments demonstrate that the mean squared error (MSE) of the dispersive pixel shifts calibrated by the proposed numerical method is 0.1774, which indicates the calibration result of the proposed method is consistent with the directly calibrated result. Furthermore, after this numerical calculation, the spectral signatures of the reconstructed cubes of the DAP-based PBCHI system show consistency with the ground truth. This work will benefit the design and calibration of the DAP-based hyperspectral imager.

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