SASSI - Super-Pixelated Adaptive Spatio-Spectral Imaging

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

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce a novel video-rate hyperspectral imager with high spatial, temporal and spectral resolutions. Our key hypothesis is that spectral profiles of pixels within each super-pixel tend to be similar. Hence, a scene-adaptive spatial sampling of a 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 \times 900600×900 pixels, at a spectral resolution of 10 nm over visible wavebands, and achieving a frame rate at 18fps.

Original languageEnglish (US)
Article number9415174
Pages (from-to)2233-2244
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume43
Issue number7
DOIs
StatePublished - Jul 1 2021

Keywords

  • Computational photography
  • adaptive imaging
  • hyperspectral fusion
  • hyperspectral imaging
  • superpixels

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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