A new approach to the interpretation of <scp>XRF</scp> spectral imaging data using neural networks

Kogou, Sotiria; Lee, Lynn; Shahtahmassebi, Golnaz; Liang, Haida

doi:10.1002/xrs.3188

Cited by 27 publications

(21 citation statements)

References 27 publications

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“…46 Julia is oen considered an order of magnitude faster than Python or R, [46][47][48] the languages regularly used thus far to process MA-XRF or hyperspectral imaging datasets. 24,34 Furthermore, in order to improve performance, Julia does not require the vectorization or other optimization of the data like is oen needed in R or Python. Consequently, with the ever-growing size of the datasets under study, this algorithm represents a novel and suitable option for data scientists working with complex, large datasets.…”

Section: Model Definition: a Non-negative K-svd Approachmentioning

confidence: 99%

XRFast a new software package for processing of MA-XRF datasets using machine learning

Vermeulen

McGeachy

et al. 2022

J. Anal. At. Spectrom.

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Section: Model Definition: a Non-negative K-svd Approachmentioning

confidence: 99%

XRFast a new software package for processing of MA-XRF datasets using machine learning

Vermeulen

McGeachy

et al. 2022

J. Anal. At. Spectrom.

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“…Martins et al proposed denoising XRF volumes using multivariate curve resolution-alternating least squares (MCR-ALS), a simple dictionary learning approach in the spectral domain to separate elemental compositions [4,5]. Kogou et al used an unsupervised learning method called self-organizing maps (SOMs) that also extracts a set of spectral dictionary atoms to decompose the XRF volumes into a representative basis [6]. This method effectively uses kmeans clustering to generate the set of dictionary endmembers.…”

Section: Related Workmentioning

confidence: 99%

Denoising Fast X-Ray Fluorescence Raster Scans of Paintings

Chopp¹,

McGeachy²,

Alfeld³

et al. 2022

Preprint

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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.

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“…Usually, ANNs require training to calculate the weights associated with each neuron, and then the weights can be employed to analyze the dataset. In the work of Kogou et al [26], the authors use a Self-Organizing Map (SOM) method that skips the training process using the same dataset as a training set, allowing a fully automated clustering.…”

Section: State Of the Art Data Handling And Synergic Applicationsmentioning

confidence: 99%

More than XRF Mapping: STEAM (Statistically Tailored Elemental Angle Mapper) a Pioneering Analysis Protocol for Pigment Studies

et al. 2021

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Among the possible variants of X-Ray Fluorescence (XRF), applications exploiting scanning Macro-XRF (MA-XRF) are lately widespread as they allow the visualization of the element distribution maintaining a non-destructive approach. The surface is scanned with a focused or collimated X-ray beam of millimeters or less: analyzing the emitted fluorescence radiation, also elements present below the surface contribute to the elemental distribution image obtained, due to the penetrative nature of X-rays. The importance of this method in the investigation of historical paintings is so obvious—as the elemental distribution obtained can reveal hidden sub-surface layers, including changes made by the artist, or restorations, without any damage to the object—that recently specific international conferences have been held. The present paper summarizes the advantages and limitations of using MA-XRF considering it as an imaging technique, in synergy with other hyperspectral methods, or combining it with spot investigations. The most recent applications in the cultural Heritage field are taken into account, demonstrating how obtained 2D-XRF maps can be of great help in the diagnostic applied on Cultural Heritage materials. Moreover, a pioneering analysis protocol based on the Spectral Angle Mapper (SAM) algorithm is presented, unifying the MA-XRF standard approach with punctual XRF, exploiting information from the mapped area as a database to extend the comprehension to data outside the scanned region, and working independently from the acquisition set-up. Experimental application on some reference pigment layers and a painting by Giotto are presented as validation of the proposed method.

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A new approach to the interpretation of XRF spectral imaging data using neural networks

Cited by 27 publications

References 27 publications

XRFast a new software package for processing of MA-XRF datasets using machine learning

XRFast a new software package for processing of MA-XRF datasets using machine learning

Denoising Fast X-Ray Fluorescence Raster Scans of Paintings

More than XRF Mapping: STEAM (Statistically Tailored Elemental Angle Mapper) a Pioneering Analysis Protocol for Pigment Studies

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