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Title: Spatial-Spectral Representation for X-Ray Fluorescence Image Super-Resolution

Abstract

X-ray fluorescence (XRF) scanning of works of art is becoming an increasing popular nondestructive analyticalmethod. The high quality XRF spectra is necessary to obtain significant information on both major and minor elements used for characterization and provenance analysis. However, there is a tradeoff between the spatial resolution of an XRF scan and the signalto- noise ratio (SNR) of each pixel’s spectrum, due to the limited scanning time. In this project, we propose an XRF image super-resolution method to address this tradeoff; thus, obtaining a high spatial resolution XRF scan with high SNR. We fuse a lowresolution XRF image and a conventional RGB high-resolution image into a product of both high spatial and high spectral resolution XRF image. There is no guarantee of a one to one mapping between XRF spectrum and RGB color since, for instance, paintings with hidden layers cannot be detected in visible but can in X-ray wavelengths. We separate the XRF image into the visible and nonvisible components. The spatial resolution of the visible component is increased utilizing the high-resolution RGB image, whereas the spatial resolution of the non-visible component is increased using a total variation super-resolution method. Finally, the visible and nonvisible components are combined tomore » obtain the final result.« less

Authors:
; ; ; ; ORCiD logo
Publication Date:
Research Org.:
Northwestern Univ., Evanston, IL (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
OSTI Identifier:
1487462
Grant/Contract Number:  
[NA0002520]
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Computational Imaging (CD-ROM)
Additional Journal Information:
[Journal Name: IEEE Transactions on Computational Imaging (CD-ROM); Journal Volume: 3; Journal Issue: 3]; Journal ID: ISSN 2334-0118
Publisher:
IEEE
Country of Publication:
United States
Language:
English

Citation Formats

Dai, Qiqin, Pouyet, Emeline, Cossairt, Oliver, Walton, Marc, and Katsaggelos, Aggelos K. Spatial-Spectral Representation for X-Ray Fluorescence Image Super-Resolution. United States: N. p., 2017. Web. doi:10.1109/TCI.2017.2703987.
Dai, Qiqin, Pouyet, Emeline, Cossairt, Oliver, Walton, Marc, & Katsaggelos, Aggelos K. Spatial-Spectral Representation for X-Ray Fluorescence Image Super-Resolution. United States. doi:10.1109/TCI.2017.2703987.
Dai, Qiqin, Pouyet, Emeline, Cossairt, Oliver, Walton, Marc, and Katsaggelos, Aggelos K. Fri . "Spatial-Spectral Representation for X-Ray Fluorescence Image Super-Resolution". United States. doi:10.1109/TCI.2017.2703987. https://www.osti.gov/servlets/purl/1487462.
@article{osti_1487462,
title = {Spatial-Spectral Representation for X-Ray Fluorescence Image Super-Resolution},
author = {Dai, Qiqin and Pouyet, Emeline and Cossairt, Oliver and Walton, Marc and Katsaggelos, Aggelos K.},
abstractNote = {X-ray fluorescence (XRF) scanning of works of art is becoming an increasing popular nondestructive analyticalmethod. The high quality XRF spectra is necessary to obtain significant information on both major and minor elements used for characterization and provenance analysis. However, there is a tradeoff between the spatial resolution of an XRF scan and the signalto- noise ratio (SNR) of each pixel’s spectrum, due to the limited scanning time. In this project, we propose an XRF image super-resolution method to address this tradeoff; thus, obtaining a high spatial resolution XRF scan with high SNR. We fuse a lowresolution XRF image and a conventional RGB high-resolution image into a product of both high spatial and high spectral resolution XRF image. There is no guarantee of a one to one mapping between XRF spectrum and RGB color since, for instance, paintings with hidden layers cannot be detected in visible but can in X-ray wavelengths. We separate the XRF image into the visible and nonvisible components. The spatial resolution of the visible component is increased utilizing the high-resolution RGB image, whereas the spatial resolution of the non-visible component is increased using a total variation super-resolution method. Finally, the visible and nonvisible components are combined to obtain the final result.},
doi = {10.1109/TCI.2017.2703987},
journal = {IEEE Transactions on Computational Imaging (CD-ROM)},
number = [3],
volume = [3],
place = {United States},
year = {2017},
month = {9}
}

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