TL;DR
This paper presents a fast, scene-adaptive hyperspectral imaging method that uses super-pixel segmentation and RGB guidance to achieve high-resolution spectral video at real-time frame rates.
Contribution
It introduces a novel super-pixel guided hyperspectral imaging technique that combines RGB images with spectral measurements for high-quality, high-resolution hyperspectral video reconstruction.
Findings
Achieves 600x900 spatial resolution at 18 fps
Provides spectral resolution of 10 nm over visible bands
Demonstrates effectiveness through simulations and lab prototype
Abstract
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…
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