A Connected Auto-Encoders Based Approach for Image Separation with Side Information: With Applications to Art Investigation.

Wei Pu, Barak Sober, Nathan Daly, Catherine Higgitt, Ingrid Daubechies, Miguel R.D. Rodrigues

Research output: Contribution to journalArticlepeer-review

Abstract

X-radiography is a widely used imaging technique in art investigation, whether to investigate the condition of a painting or provide insights into artists’ techniques and working methods. In this paper, we propose a new architecture based on the use of ‘connected’ auto-encoders in order to separate mixed X-ray images acquired from double-sided paintings, where in addition to the mixed X-ray image one can also exploit the two RGB images associated with the front and back of the painting. This proposed architecture uses convolutional auto-encoders that extract features from the RGB images that can be employed to (1) reproduce both of the original RGB images, (2) reconstruct the associated separated X-ray images, and (3) regenerate the mixed X-ray image. It operates in a totally self-supervised fashion without the need for examples containing both the mixed X-ray images and the separated ones. Based on images from the double-sided wing panels from the famous Ghent Altarpiece, painted in

Keywords

  • Signal Processing and Analysis
  • X-ray imaging
  • Painting
  • Decoding
  • Art
  • Image reconstruction
  • Deep learning
  • Training
  • Image separation with side information
  • deep neural networks
  • convolutional neural networks
  • auto-encoders

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