Texture-adaptive hyperspectral video acquisition system with a spatial light modulator

Fang, Xiaojing; Feng, Jing; Wang, Yongjin

doi:10.1117/12.2071445

Cited by 1 publication

(1 citation statement)

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“…In the context of snapshot hyperspectral imaging, Feng et al [28] and Ma et al [29] showed that an RGB image can be used to tailor the mask for CASSI, thereby creating a content-adaptive hyperspectral sampling strategy. This is a promising approach to snapshot hyperspectral imaging, and in many ways, is a key motivation for our own work.…”

Section: Adaptive Sampling Strategiesmentioning

confidence: 99%

SASSI -- Super-Pixelated Adaptive Spatio-Spectral Imaging

Saragadam¹,

DeZeeuw²,

Baraniuk³

et al. 2020

Preprint

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We introduce a novel video-rate hyperspectral imager with high spatial, and temporal resolutions. Our key hypothesis is that spectral profiles of pixels in a super-pixel of an oversegmented image tend to be very similar. Hence, a scene-adaptive spatial sampling of an hyperspectral scene, guided by its super-pixel segmented image, is capable of obtaining high-quality reconstructions. To achieve this, we acquire an RGB image of the scene, compute its super-pixels, from which we generate a spatial mask of locations where we measure high-resolution spectrum. The hyperspectral image is subsequently estimated by fusing the RGB image and the spectral measurements using a learnable guided filtering approach. Due to low computational complexity of the superpixel estimation step, our setup can capture hyperspectral images of the scenes with little overhead over traditional snapshot hyperspectral cameras, but with significantly higher spatial and spectral resolutions. We validate the proposed technique with extensive simulations as well as a lab prototype that measures hyperspectral video at a spatial resolution of 600 × 900 pixels, at a spectral resolution of 10 nm over visible wavebands, and achieving a frame rate at 18fps.

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Section: Adaptive Sampling Strategiesmentioning

confidence: 99%