X-Ray fluorescence image super-resolution using dictionary learning

Qiqin Dai, Emeline Pouyet, Oliver Cossairt, Marc Walton, Francesca Casadio, Aggelos Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

X-Ray fluorescence (XRF) scanning of works of art is becoming an increasingly popular non-destructive analytical method. 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 trade-off between the spatial resolution of an XRF scan and the Signal-to-Noise Ratio (SNR) of each pixel's spectrum, due to the limited scanning time. In this paper, we propose an XRF image super-resolution method to address this trade-off, thus obtaining a high spatial resolution XRF scan with high SNR. We use a sparse representation of each pixel using a dictionary trained from the spectrum samples of the image, while imposing a spatial smoothness constraint on the sparse coefficients. We then increase the spatial resolution of the sparse coefficient map using a conventional super-resolution method. Finally the high spatial resolution XRF image is reconstructed by the high spatial resolution sparse coefficient map and the trained spectrum dictionary.

Original languageEnglish (US)
Title of host publication2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509019298
DOIs
StatePublished - Aug 1 2016
Event12th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016 - Bordeaux, France
Duration: Jul 11 2016Jul 12 2016

Publication series

Name2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016

Other

Other12th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016
CountryFrance
CityBordeaux
Period7/11/167/12/16

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Keywords

  • X-ray fluorescence
  • dictionary learning
  • sparse coding
  • super-resolution

ASJC Scopus subject areas

  • Media Technology
  • Signal Processing

Cite this

Dai, Q., Pouyet, E., Cossairt, O., Walton, M., Casadio, F., & Katsaggelos, A. (2016). X-Ray fluorescence image super-resolution using dictionary learning. In 2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016 [7528182] (2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVMSPW.2016.7528182