Mixed X-Ray Image Separation for Artworks With Concealed Designs-Reference-Cited by-同舟云学术

Mixed X-Ray Image Separation for Artworks With Concealed Designs

Published:2022 Issue: Volume:31 Page:4458-4473
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Pu Wei¹,Huang Jun-Jie²^ORCID,Sober Barak³,Daly Nathan⁴^ORCID,Higgitt Catherine⁴^ORCID,Daubechies Ingrid⁵,Dragotti Pier Luigi⁶^ORCID,Rodrigues Miguel R. D.¹^ORCID

Affiliation:

1. Department of Electronic and Electrical Engineering, University College London, London, U.K

2. College of Computer Science, National University of Defense Technology, Changsha, China

3. Department of Statistics and Data Science, The Hebrew University of Jerusalem, Jerusalem, Israel

4. Scientific Department, National Gallery, London, U.K

5. Department of Electrical and Computer Engineering and the Department of Mathematics, Duke University, Durham, NC, USA

6. Department of Electrical and Electronic Engineering, Imperial College London, London, U.K

Funder

Engineering and Physical Sciences Research Council

Royal Society

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/9626658/09810038.pdf?arnumber=9810038

Reference48 articles.

1. Macroscopic X-ray Powder Diffraction Scanning, a New Method for Highly Selective Chemical Imaging of Works of Art: Instrument Optimization

2. Learned Convolutional Sparse Coding

3. Towards Unsupervised Single-channel Blind Source Separation Using Adversarial Pair Unmix-and-remix

4. Generative adversarial networks;goodfellow;arXiv 1406 2661,2014

5. Neural separation of observed and unobserved distributions;halperin;arXiv 1811 12739,2018

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