Restoration of X-ray fluorescence images of hidden paintings

Anitha, Anila; Brasoveanu, Andrei; Duarte, Marco F.; Hughes, Susan M.; Daubechies, Ingrid; Dik, Joris; Janssens, Koen; Alfeld, Matthias

doi:10.1016/j.sigpro.2012.09.027

Cited by 32 publications

(13 citation statements)

References 34 publications

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“…In particular, XRF has been used extensively to investigate historical paintings by capturing the elemental distribution images of their complex layered structure. This method reveals the painting history from the artist creation to restoration processes [30], [31].…”

Section: Xrf Imaging Using Adaptive Samplingmentioning

confidence: 99%

Adaptive Image Sampling Using Deep Learning and Its Application on X-Ray Fluorescence Image Reconstruction

Dai

Chopp

Pouyet

et al. 2020

IEEE Trans. Multimedia

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This paper presents an adaptive image sampling algorithm based on Deep Learning (DL). It consists of an adaptive sampling mask generation network which is jointly trained with an image inpainting network. The sampling rate is controlled by the mask generation network, and a binarization strategy is investigated to make the sampling mask binary. In addition to the image sampling and reconstruction process, we show how it can be extended and used to speed up raster scanning such as the X-Ray fluorescence (XRF) image scanning process. Recently XRF laboratory-based systems have evolved into lightweight and portable instruments thanks to technological advancements in both X-Ray generation and detection. However, the scanning time of an XRF image is usually long due to the long exposure requirements (e.g., 100µs − 1ms per point). We propose an XRF image inpainting approach to address the long scanning times, thus speeding up the scanning process, while being able to reconstruct a high quality XRF image. The proposed adaptive image sampling algorithm is applied to the RGB image of the scanning target to generate the sampling mask. The XRF scanner is then driven according to the sampling mask to scan a subset of the total image pixels. Finally, we inpaint the scanned XRF image by fusing the RGB image to reconstruct the full scan XRF image. The experiments show that the proposed adaptive sampling algorithm is able to effectively sample the image and achieve a better reconstruction accuracy than that of existing methods.

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Section: Xrf Imaging Using Adaptive Samplingmentioning

confidence: 99%

Adaptive Image Sampling Using Deep Learning and Its Application on X-Ray Fluorescence Image Reconstruction

Dai

Chopp

Pouyet

et al. 2020

IEEE Trans. Multimedia

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“…A crucial ingredient for a good performance of the non-local algorithm is its initialisation. In particular, once the inpainting domain is known, a pre-processing step where a local inpainting model, such as the TV inpainting model (5) with (6), can be run to provide a rough, but reliable initialisation of the algorithm [3] .…”

Section: Exemplar-based Inpaintingmentioning

confidence: 99%

Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts

et al. 2018

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The last 50 years have seen an impressive development of mathematical methods for the analysis and processing of digital images, mostly in the context of photography, biomedical imaging and various forms of engineering. The arts have been mostly overlooked in this process, apart from a few exceptional works in the last 10 years. With the rapid emergence of digitisation in the arts, however, the arts domain is becoming increasingly receptive to digital image processing methods and the importance of paying attention to this therefore increases. In this paper we discuss a range of mathematical methods for digital image restoration and digital visualisation for illuminated manuscripts. The latter provide an interesting opportunity for digital manipulation because they traditionally remain physically untouched. At the same time they also serve as an example for the possibilities mathematics and digital restoration offer as a generic and objective toolkit for the arts. Electronic supplementary material The online version of this article (10.1186/s40494-018-0216-z) contains supplementary material, which is available to authorized users.

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“…In these studies the thickness of paint layers and covering glaze layers was calculated [105,106]. Further, advanced data processing routines have been applied in order to remove absorption artefacts from elemental distribution images and reconstruct overpainted artworks [107].…”

Section: Scanning Macro-xrfmentioning

confidence: 99%

Mobile depth profiling and sub-surface imaging techniques for historical paintings—A review

Alfeld

Broekaert

2013

Spectrochimica Acta Part B: Atomic Spectroscopy

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114

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Restoration of X-ray fluorescence images of hidden paintings

Cited by 32 publications

References 34 publications

Adaptive Image Sampling Using Deep Learning and Its Application on X-Ray Fluorescence Image Reconstruction

Adaptive Image Sampling Using Deep Learning and Its Application on X-Ray Fluorescence Image Reconstruction

Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts

Mobile depth profiling and sub-surface imaging techniques for historical paintings—A review

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