Dictionary based Hyperspectral Image Reconstruction Captured with CS-MUSI

Yaniv Oiknine, Boaz Arad, Isaac August, Ohad Ben-Shahar, Adrian Stern

Research output: Contribution to conferencePaperpeer-review

3 Scopus citations

Abstract

The Compressive Sensing Miniature Ultra-Spectral Imaging (CS-MUSI) camera uses a spectral modulator and a grayscale sensor in order to capture an encoded compressed spectral signal. Using the compressive sensing (CS) theory hyperspectral (HS) cubes with hundreds of spectral bands can be reconstructed from an order of magnitude fewer samples. In this work, we show that by using spectral dictionary, as the sparsifying operator, for reconstruction of CS HS images acquired with our CS-MUSI camera, we can both increase the reconstruction quality and reduce the number of measurements CS theory requires as well.

Original languageEnglish
DOIs
StatePublished - 1 Sep 2018
Event9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2018 - Amsterdam, Netherlands
Duration: 23 Sep 201826 Sep 2018

Conference

Conference9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2018
Country/TerritoryNetherlands
CityAmsterdam
Period23/09/1826/09/18

Keywords

  • CS-MUSI
  • Compressive sensing
  • Dictionary
  • Hyperspectral
  • Sparsifying operator

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