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- Hyperspectral image reconstruction of heritage artwork using RGB images and deep neural networksPublication . Chen, Ailin; Jesus, Rui; Vilarigues, M.The application of our research is in the art world where the scarcity of available analytical data from a particular artist or physical access for its acquisition is restricted. This poses a fundamental problem for the purpose of conservation, restoration or authentication of historical artworks. We address part of this problem by providing a practical method to generate hyperspectral data from readily available RGB imagery of artwork by means of a two-step process using deep neural networks. The particularities of our approach include the generation of learnable colour mixtures and reflectances from a reduced collection of prior data for the mapping and reconstruction of hyperspectral features on new images. Further analysis and correction of the prediction are achieved by a second network that reduces the error by producing results akin to those obtained by a hyperspectral camera. Our method has been used to study a collection of paintings by Amadeo de Souza-Cardoso where successful results were obtained. CCS CONCEPTS • Computing methodologies → Neural networks; Artificial intelligence; • Applied computing → Arts and humanities.
- Convolutional neural network-based pure paint pigment identification using hyperspectral imagesPublication . Chen, Ailin; Jesus, Rui; VilariguesThis research presents the results of the implementation of deep learning neural networks in the identification of pure pigments of heritage artwork, namely paintings. Our paper applies an inno vative three-branch deep learning model to maximise the correct identification of pure pigments. The model proposed combines the feature maps obtained from hyperspectral images through multi ple convolutional neural networks, and numerical, hyperspectral metric data with respect to a set of reference reflectances. The results obtained exhibit an accurate representation of the pure pre dicted pigments which are confirmed through the use of analytical techniques. The model presented outperformed the compared coun terparts and is deemed to be an important direction, not only in terms of utilisation of hyperspectral data and concrete pigment data in heritage analysis, but also in the application of deep learning in other fields.