X-ray images are widely used in the study of paintings. When a painting has hidden sub-surface features (e.g., reuse of the canvas or revision of a composition by the artist), the resulting X-ray images can be hard to interpret as they include contributions from both the surface painting and the hidden design. In this paper we propose a self-supervised deep learning-based image separation approach that can be applied to the X-ray images from such paintings ('mixed X-ray images') to separate them into two hypothetical X-ray images, one containing information related to the visible painting only and the other containing the hidden features. The proposed approach involves two steps: (1) separation of the mixed X-ray image into two images, guided by the combined use of a reconstruction and an exclusion loss; (2) even allocation of the error map into the two individual, separated X-ray images, yielding separation results that have an appearance that is more familiar in relation to X-ray images. The proposed method was demonstrated on a real painting with hidden content, Doña Isabel de Porcel by Francisco de Goya, to show its effectiveness.
|Title of host publication||2021 29th European Signal Processing Conference (EUSIPCO)|
|Publisher||European Signal Processing Conference, EUSIPCO|
|Number of pages||5|
|State||Published - 2021|
|Event||29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland|
Duration: 23 Aug 2021 → 27 Aug 2021
|Name||European Signal Processing Conference|
|Conference||29th European Signal Processing Conference, EUSIPCO 2021|
|Period||23/08/21 → 27/08/21|
Bibliographical notePublisher Copyright:
© 2021 European Signal Processing Conference. All rights reserved.
- Art investigation
- Convolutional neural networks
- Deep neural networks
- Image separation