@inproceedings{7636c06f5977401b951ee7a152388c09,
title = "X-Ray fluorescence image super-resolution using dictionary learning",
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.",
keywords = "X-ray fluorescence, dictionary learning, sparse coding, super-resolution",
author = "Qiqin Dai and Emeline Pouyet and Oliver Cossairt and Marc Walton and Francesca Casadio and Aggelos Katsaggelos",
year = "2016",
month = aug,
day = "1",
doi = "10.1109/IVMSPW.2016.7528182",
language = "English (US)",
series = "2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016",
address = "United States",
note = "12th IEEE Image, Video, and Multidimensional Signal Processing Workshop, IVMSP 2016 ; Conference date: 11-07-2016 Through 12-07-2016",
}