@inproceedings{Qiqin2016a,
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.},
author = {Dai, Qiqin and Pouyet, Emeline and Cossairt, Oliver and Walton, Marc and Casadio, Francesca and Katsaggelos, Aggelos},
booktitle = {2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)},
doi = {10.1109/IVMSPW.2016.7528182},
isbn = {978-1-5090-1929-8},
keywords = {X-ray fluorescence,dictionary learning,sparse coding,super-resolution},
month = {jul},
pages = {1--5},
publisher = {IEEE},
title = {{X-Ray fluorescence image super-resolution using dictionary learning}},
url = {http://ieeexplore.ieee.org/document/7528182/},
year = {2016}
}

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