@inproceedings{3dcd28be8fd44f40b1ca856f9a322828,
title = "Dictionary based Hyperspectral Image Reconstruction Captured with CS-MUSI",
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.",
keywords = "CS-MUSI, Compressive sensing, Dictionary, Hyperspectral, Sparsifying operator",
author = "Yaniv Oiknine and Boaz Arad and Isaac August and Ohad Ben-Shahar and Adrian Stern",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2018 ; Conference date: 23-09-2018 Through 26-09-2018",
year = "2018",
month = sep,
day = "1",
doi = "10.1109/WHISPERS.2018.8747233",
language = "American English",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
booktitle = "2018 9th Workshop on Hyperspectral Image and Signal Processing",
}