Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks

We apply generative adversarial convolutional neural networks to the problem of style transfer to underdrawings and ghost-images in x-rays of fine art paintings with a special focus on enhancing their spatial resolution. We build upon a neural architecture developed for the related problem of synthesizing high-resolution photo-realistic image from semantic label maps. Our neural architecture achieves high resolution through a hierarchy of generators and discriminator sub-networks, working throughout a range of spatial resolutions. This coarse-to-fine generator architecture can increase the effective resolution by a factor of eight in each spatial direction, or an overall increase in number of pixels by a factor of 64. We also show that even just a few examples of human-generated image segmentations can greatly improve -- qualitatively and quantitatively -- the generated images. We demonstrate our method on works such as Leonardos Madonna of the carnation and the underdrawing in his Virgin of the rocks, which pose several special problems in style transfer, including the paucity of representative works from which to learn and transfer style information.

Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars

We describe the application of convolutional neural network style transfer to the problem of improved visualization of underdrawings and ghost-paintings in fine art oil paintings. Such underdrawings and hidden paintings are typically revealed by x-ray or infrared techniques which yield images that are grayscale, and thus devoid of color and full style information. Past methods for inferring color in underdrawings have been based on physical x-ray fluorescence spectral imaging of pigments in ghost-paintings and are thus expensive, time consuming, and require equipment not available in most conservation studios. Our algorithmic methods do not need such expensive physical imaging devices. Our proof-of-concept system, applied to works by Pablo Picasso and Leonardo, reveal colors and designs that respect the natural segmentation in the ghost-painting. We believe the computed images provide insight into the artist and associated oeuvre not available by other means. Our results strongly suggest that future applications based on larger corpora of paintings for training will display color schemes and designs that even more closely resemble works of the artist. For these reasons refinements to our methods should find wide use in art conservation, connoisseurship, and art analysis.

Ares: A Mars model retrieval framework for ExoMars Trace Gas Orbiter NOMAD solar occultation measurements

Ares is an extension of the TauREx 3 retrieval framework for the Martian atmosphere. Ares is a collection of new atmospheric parameters and forward models, designed for the European Space Agencys (ESA) Trace Gas Orbiter (TGO) Nadir and Occultation for MArs Discovery (NOMAD) instrument, Solar Occultation (SO) channel. Ares provides unique insights into the chemical composition of the Martian atmosphere by applying methods utilised in exoplanetary atmospheric retrievals, Waldmann et al. (2015), Al-Refaie et al. (2019). This insight may help unravel the true nature of $CH_{4}$ on Mars. The Ares model is here described. Subsequently, the components of Ares are defined, including; the NOMAD SO channel instrument function model, Martian atmospheric molecular absorption cross-sections, geometry models, and a NOMAD noise model. Ares atmospheric priors and forward models are presented, (i.e., simulated NOMAD observations), and are analysed, compared and validated against the Planetary Spectrum Generator, Villanueva et al. (2018).