Hidden Knowledge: Mathematical Methods for the Extraction of the Fingerprint of Medieval Paper from Digital Images

1 code implementation • 7 Mar 2023 • Tamara G. Grossmann, Carola-Bibiane Schönlieb, Orietta Da Rold

This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet.

Can Physics-Informed Neural Networks beat the Finite Element Method?

1 code implementation • 8 Feb 2023 • Tamara G. Grossmann, Urszula Julia Komorowska, Jonas Latz, Carola-Bibiane Schönlieb

In terms of solution time and accuracy, physics-informed neural networks have not been able to outperform the finite element method in our study.

Unsupervised Learning of the Total Variation Flow

1 code implementation • 9 Jun 2022 • Tamara G. Grossmann, Sören Dittmer, Yury Korolev, Carola-Bibiane Schönlieb

Inspired by and extending the framework of physics-informed neural networks (PINNs), we propose the TVflowNET, an unsupervised neural network approach, to approximate the solution of the TV flow given an initial image and a time instance.

Texture Classification

Deeply Learned Spectral Total Variation Decomposition

1 code implementation • NeurIPS 2020 • Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola-Bibiane Schönlieb

To the best of our knowledge, this is the first approach towards learning a non-linear spectral decomposition of images.