Denoising Fast X-Ray Fluorescence Raster Scans of Paintings. Chopp, H., McGeachy, A., Alfeld, M., Cossairt, O., Walton, M., & Katsaggelos, A. arXiv preprint arXiv:2206.01740, jun, 2022. ![Denoising Fast X-Ray Fluorescence Raster Scans of Paintings [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex Macro x-ray fluorescence (XRF) imaging of cultural heritage objects, while a popular non-invasive technique for providing elemental distribution maps, is a slow acquisition process in acquiring high signal-to-noise ratio XRF volumes. Typically on the order of tenths of a second per pixel, a raster scanning probe counts the number of photons at different energies emitted by the object under x-ray illumination. In an effort to reduce the scan times without sacrificing elemental map and XRF volume quality, we propose using dictionary learning with a Poisson noise model as well as a color image-based prior to restore noisy, rapidly acquired XRF data.
@article{Henry2022,
abstract = {Macro x-ray fluorescence (XRF) imaging of cultural heritage objects, while a popular non-invasive technique for providing elemental distribution maps, is a slow acquisition process in acquiring high signal-to-noise ratio XRF volumes. Typically on the order of tenths of a second per pixel, a raster scanning probe counts the number of photons at different energies emitted by the object under x-ray illumination. In an effort to reduce the scan times without sacrificing elemental map and XRF volume quality, we propose using dictionary learning with a Poisson noise model as well as a color image-based prior to restore noisy, rapidly acquired XRF data.},
archivePrefix = {arXiv},
arxivId = {2206.01740},
author = {Chopp, Henry and McGeachy, Alicia and Alfeld, Matthias and Cossairt, Oliver and Walton, Marc and Katsaggelos, Aggelos},
eprint = {2206.01740},
journal = {arXiv preprint arXiv:2206.01740},
month = {jun},
title = {{Denoising Fast X-Ray Fluorescence Raster Scans of Paintings}},
url = {http://arxiv.org/abs/2206.01740},
year = {2022}
}
Downloads: 0
{"_id":"7Je68tpCbmZiZENkx","bibbaseid":"chopp-mcgeachy-alfeld-cossairt-walton-katsaggelos-denoisingfastxrayfluorescencerasterscansofpaintings-2022","author_short":["Chopp, H.","McGeachy, A.","Alfeld, M.","Cossairt, O.","Walton, M.","Katsaggelos, A."],"bibdata":{"bibtype":"article","type":"article","abstract":"Macro x-ray fluorescence (XRF) imaging of cultural heritage objects, while a popular non-invasive technique for providing elemental distribution maps, is a slow acquisition process in acquiring high signal-to-noise ratio XRF volumes. Typically on the order of tenths of a second per pixel, a raster scanning probe counts the number of photons at different energies emitted by the object under x-ray illumination. In an effort to reduce the scan times without sacrificing elemental map and XRF volume quality, we propose using dictionary learning with a Poisson noise model as well as a color image-based prior to restore noisy, rapidly acquired XRF data.","archiveprefix":"arXiv","arxivid":"2206.01740","author":[{"propositions":[],"lastnames":["Chopp"],"firstnames":["Henry"],"suffixes":[]},{"propositions":[],"lastnames":["McGeachy"],"firstnames":["Alicia"],"suffixes":[]},{"propositions":[],"lastnames":["Alfeld"],"firstnames":["Matthias"],"suffixes":[]},{"propositions":[],"lastnames":["Cossairt"],"firstnames":["Oliver"],"suffixes":[]},{"propositions":[],"lastnames":["Walton"],"firstnames":["Marc"],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["Aggelos"],"suffixes":[]}],"eprint":"2206.01740","journal":"arXiv preprint arXiv:2206.01740","month":"jun","title":"Denoising Fast X-Ray Fluorescence Raster Scans of Paintings","url":"http://arxiv.org/abs/2206.01740","year":"2022","bibtex":"@article{Henry2022,\nabstract = {Macro x-ray fluorescence (XRF) imaging of cultural heritage objects, while a popular non-invasive technique for providing elemental distribution maps, is a slow acquisition process in acquiring high signal-to-noise ratio XRF volumes. Typically on the order of tenths of a second per pixel, a raster scanning probe counts the number of photons at different energies emitted by the object under x-ray illumination. In an effort to reduce the scan times without sacrificing elemental map and XRF volume quality, we propose using dictionary learning with a Poisson noise model as well as a color image-based prior to restore noisy, rapidly acquired XRF data.},\narchivePrefix = {arXiv},\narxivId = {2206.01740},\nauthor = {Chopp, Henry and McGeachy, Alicia and Alfeld, Matthias and Cossairt, Oliver and Walton, Marc and Katsaggelos, Aggelos},\neprint = {2206.01740},\njournal = {arXiv preprint arXiv:2206.01740},\nmonth = {jun},\ntitle = {{Denoising Fast X-Ray Fluorescence Raster Scans of Paintings}},\nurl = {http://arxiv.org/abs/2206.01740},\nyear = {2022}\n}\n","author_short":["Chopp, H.","McGeachy, A.","Alfeld, M.","Cossairt, O.","Walton, M.","Katsaggelos, A."],"key":"Henry2022","id":"Henry2022","bibbaseid":"chopp-mcgeachy-alfeld-cossairt-walton-katsaggelos-denoisingfastxrayfluorescencerasterscansofpaintings-2022","role":"author","urls":{"Paper":"http://arxiv.org/abs/2206.01740"},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://sites.northwestern.edu/ivpl/files/2023/06/IVPL_Updated_publications-1.bib","dataSources":["KTWAakbPXLGfYseXn","ePKPjG8C6yvpk4mEK","zFPgsTDAW8aDnb5iN","E6Bth2QB5BYjBMZE7","nbnEjsN7MJhurAK9x","PNQZj6FjzoxxJk4Yi","7FpDWDGJ4KgpDiGfB","bod9ms4MQJHuJgPpp","QR9t5P2cLdJuzhfzK","D8k2SxfC5dKNRFgro","7Dwzbxq93HWrJEhT6","qhF8zxmGcJfvtdeAg","fvDEHD49E2ZRwE3fb","H7crv8NWhZup4d4by","DHqokWsryttGh7pJE","vRJd4wNg9HpoZSMHD","sYxQ6pxFgA59JRhxi","w2WahSbYrbcCKBDsC","XasdXLL99y5rygCmq","3gkSihZQRfAD2KBo3","t5XMbyZbtPBo4wBGS","bEpHM2CtrwW2qE8FP","teJzFLHexaz5AQW5z"],"keywords":[],"search_terms":["denoising","fast","ray","fluorescence","raster","scans","paintings","chopp","mcgeachy","alfeld","cossairt","walton","katsaggelos"],"title":"Denoising Fast X-Ray Fluorescence Raster Scans of Paintings","year":2022}