Abstract
In this paper, we focus on X-ray images (X-radiographs) of paintings with concealed sub-surface designs ( e.g. , deriving from reuse of the painting support or revision of a composition by the artist), which therefore include contributions from both the surface painting and the concealed features. In particular, we propose a self-supervised deep learning-based image separation approach that can be applied to the X-ray images from such paintings to separate them into two hypothetical X-ray images. One of these reconstructed images is related to the X-ray image of the concealed painting, while the second one contains only information related to the X-ray image of the visible painting. The proposed separation network consists of two components: the analysis and the synthesis sub-networks. The analysis sub-network is based on learned coupled iterative shrinkage thresholding algorithms (LCISTA) designed using algorithm unrolling techniques, and the synthesis sub-network consists of several
| Original language | American English |
|---|---|
| Pages (from-to) | 4458 - 4473 |
| Number of pages | 16 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 31 |
| State | Published - 2022 |
Bibliographical note
Funding Information:This work was supported in part by the Engineering and Physical Sciences Research Council under Grant EP/R032785/1 and in part by the Royal Society under Grant NIF/R1/180735.
Publisher Copyright:
© 1992-2012 IEEE.
Keywords
- Signal Processing and Analysis
- Communication
- Networking and Broadcast Technologies
- Computing and Processing
- X-ray imaging
- Painting
- Imaging
- Image reconstruction
- Feature extraction
- Task analysis
- Paints
- Art investigation
- image separation
- deep neural networks
- convolutional neural networks
- unrolling technique