Image Separation With Side Information: A Connected Auto-Encoders Based Approach.

X-radiography (X-ray imaging) is a widely used imaging technique in art investigation. It can provide information about the condition of a painting as well as insights into an artist's techniques and working methods, often revealing hidden information invisible to the naked eye. X-radiograpy of double-sided paintings results in a mixed X-ray image and this paper deals with the problem of separating this mixed image. Using the visible color images (RGB images) from each side of the painting, we propose a new Neural Network architecture, based upon 'connected' auto-encoders, designed to separate the mixed X-ray image into two simulated X-ray images corresponding to each side. This connected auto-encoders architecture is such that the encoders are based on convolutional learned iterative shrinkage thresholding algorithms (CLISTA) designed using algorithm unrolling techniques, whereas the decoders consist of simple linear convolutional layers; the encoders extract sparse codes from the visible image of the front and rear paintings and mixed X-ray image, whereas the decoders reproduce both the original RGB images and the mixed X-ray image. The learning algorithm operates in a totally self-supervised fashion without requiring a sample set that contains both the mixed X-ray images and the separated ones. The methodology was tested on images from the double-sided wing panels of the Ghent Altarpiece, painted in 1432 by the brothers Hubert and Jan van Eyck. These tests show that the proposed approach outperforms other state-of-the-art X-ray image separation methods for art investigation applications.

Mixed X-Ray Image Separation for Artworks With Concealed Designs.

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 linear mappings. The learning algorithm operates in a totally self-supervised fashion without requiring a sample set that contains both the mixed X-ray images and the separated ones. The proposed method is demonstrated on a real painting with concealed content, Do na Isabel de Porcel by Francisco de Goya, to show its effectiveness.

Single-Molecule Reaction Chemistry in Patterned Nanowells.

A new approach to synthetic chemistry is performed in ultraminiaturized, nanofabricated reaction chambers. Using lithographically defined nanowells, we achieve single-point covalent chemistry on hundreds of individual carbon nanotube transistors, providing robust statistics and unprecedented spatial resolution in adduct position. Each device acts as a sensor to detect, in real-time and through quantized changes in conductance, single-point functionalization of the nanotube as well as consecutive chemical reactions, molecular interactions, and molecular conformational changes occurring on the resulting single-molecule probe. In particular, we use a set of sequential bioconjugation reactions to tether a single-strand of DNA to the device and record its repeated, reversible folding into a G-quadruplex structure. The stable covalent tether allows us to measure the same molecule in different solutions, revealing the characteristic increased stability of the G-quadruplex structure in the presence of potassium ions (K(+)) versus sodium ions (Na(+)). Nanowell-confined reaction chemistry on carbon nanotube devices offers a versatile method to isolate and monitor individual molecules during successive chemical reactions over an extended period of time.