A Data Fusion Method For The Delayering Of X-Ray Fluorescence Images Of Painted Works Of Art

Fiske, Lionel; Katsaggelos, Aggelos K.; Aalders, Maurice C. G.; Alfeld, Matthias; Walton, Marc; Cossairt, Oliver

doi:10.1109/icip42928.2021.9506300

Cited by 6 publications

(5 citation statements)

References 13 publications

(15 reference statements)

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“…This approach reduces the need for high detector energy resolution without affecting the sensitivity of benchtop XFCT. Fiske [15] presented a flexible approach involving an initial clustering step for visible hyperspectral reflectance data (RIS) and the creation of a synthetic surface XRF image. By comparing the full and synthetic surface XRF images, surface and subsurface correlated features could be identified.…”

Section: Related Workmentioning

confidence: 99%

Identification Method for XRF Spectral Analysis Based on an AGA-BP-Attention Neural Network

Chang,

Zhang,

et al. 2024

Electronics

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X-ray fluorescence (XRF) spectroscopy is a non-destructive differential measurement technique widely utilized in elemental analysis. However, due to its inherent high non-linearity and noise issues, it is challenging for XRF spectral analysis to achieve high levels of accuracy. In response to these challenges, this paper proposes a method for XRF spectral analysis that integrates an adaptive genetic algorithm with a backpropagation neural network, enhanced by an attention mechanism, termed as the AGA-BP-Attention method. By leveraging the robust feature extraction capabilities of the neural network and the ability of the attention mechanism to focus on significant features, spectral features are extracted for elemental identification. The adaptive genetic algorithm is subsequently employed to optimize the parameters of the BP neural network, such as weights and thresholds, which enhances the model’s accuracy and stability. The experimental results demonstrate that, compared to traditional BP neural networks, the AGA-BP-Attention network can more effectively address the non-linearity and noise issues of XRF spectral signals. In XRF spectral analysis of air pollutant samples, it achieved superior prediction accuracy, effectively suppressing the impact of background noise on spectral element recognition.

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Section: Related Workmentioning

confidence: 99%

Identification Method for XRF Spectral Analysis Based on an AGA-BP-Attention Neural Network

Chang,

Zhang,

et al. 2024

Electronics

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“…Inspection of CH assets: The study in [10] describes a data fusion pipeline for the delayering of X-ray fluorence (XRF) images. At first, visible hyperspectral reflectance data (RIS) is clustered in pigment mixtures.…”

Section: Related Workmentioning

confidence: 99%

Automatic Inspection of Cultural Monuments Using Deep and Tensor-Based Learning on Hyperspectral Imagery

Tzortzis

Rallis

Makantasis

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such high-dimensional data becomes challenging from the perspective of machine learning techniques to be applied. In this paper, we propose a Rank-R tensor-based learning model to identify and classify material defects on Cultural Heritage monuments. In contrast to conventional deep learning approaches, the proposed high order tensor-based learning demonstrates greater accuracy and robustness against overfitting. Experimental results on real-world data from UN-ESCO protected areas indicate the superiority of the proposed scheme compared to conventional deep learning models.

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“…For this reason, the correlation of the collected data by both techniques is essential for obtaining a comprehensive and holistic view of the objects under investigation. By combining the advantages of each mentioned modality, researchers and art conservators can achieve a more accurate, precise, and insightful analysis method [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…The first approach involves using multiple techniques within a unified system to generate inherently co-registered and synchronized data cubes [16,17]. The second approach involves using distinct systems for asynchronous imaging measurements and the subsequent data co-registration and data fusion [14,15]. The asynchronous approach may be more costeffective and less complex to implement but introduces challenges in ensuring accurate co-registration.…”

Section: Introductionmentioning

confidence: 99%

Scanning Micro X-ray Fluorescence and Multispectral Imaging Fusion: A Case Study on Postage Stamps

Gerodimos,

Patakiouta,

Papadakis

et al. 2024

J. Imaging

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Scanning micrο X-ray fluorescence (μ-XRF) and multispectral imaging (MSI) were applied to study philately stamps, selected for their small size and intricate structures. The μ-XRF measurements were accomplished using the M6 Jetstream Bruker scanner under optimized conditions for spatial resolution, while the MSI measurements were performed employing the XpeCAM-X02 camera. The datasets were acquired asynchronously. Elemental distribution maps can be extracted from the μ-XRF dataset, while chemical distribution maps can be obtained from the analysis of the multispectral dataset. The objective of the present work is the fusion of the datasets from the two spectral imaging modalities. An algorithmic co-registration of the two datasets is applied as a first step, aiming to align the multispectral and μ-XRF images and to adapt to the pixel sizes, as small as a few tens of micrometers. The dataset fusion is accomplished by applying k-means clustering of the multispectral dataset, attributing a representative spectrum to each pixel, and defining the multispectral clusters. Subsequently, the μ-XRF dataset within a specific multispectral cluster is analyzed by evaluating the mean XRF spectrum and performing k-means sub-clustering of the μ-XRF dataset, allowing the differentiation of areas with variable elemental composition within the multispectral cluster. The data fusion approach proves its validity and strength in the context of philately stamps. We demonstrate that the fusion of two spectral imaging modalities enhances their analytical capabilities significantly. The spectral analysis of pixels within clusters can provide more information than analyzing the same pixels as part of the entire dataset.

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A Data Fusion Method For The Delayering Of X-Ray Fluorescence Images Of Painted Works Of Art

Cited by 6 publications

References 13 publications

Identification Method for XRF Spectral Analysis Based on an AGA-BP-Attention Neural Network

Identification Method for XRF Spectral Analysis Based on an AGA-BP-Attention Neural Network

Automatic Inspection of Cultural Monuments Using Deep and Tensor-Based Learning on Hyperspectral Imagery

Scanning Micro X-ray Fluorescence and Multispectral Imaging Fusion: A Case Study on Postage Stamps

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