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Terahertz imaging super-resolution for documental heritage diagnostics

https://ieeexplore.ieee.org/document/10551541

Terahertz imaging provides valuable insights into the composition and structure of objects or materials, with applications spanning security screening, medical imaging, materials science, and cultural heritage preservation. Despite its widespread utility, traditional terahertz imaging is limited in spatial resolution to approximately 1 mm according to Abbe’s formula. In this paper, we propose a novel super-resolution method for terahertz time-domain spectroscopy systems. Our approach involves spatial filtering through scattering in the far-field of high spatial frequency components of the imaged sample. This method leverages evanescent wave filtering using a knife edge, akin to a standard structured illumination scheme. We demonstrate improved spatial resolution in slit diffraction, edge imaging, and reflection imaging of structures fabricated on a paper substrate using commonly encountered materials in works of art and documents. Furthermore, we present super-resolved images of an ancient document on parchment, showcasing the effectiveness of our proposed method.

EIC Project HEISINGBERG launched !

The EU project HEISINGBERG has started!

This project is funded by the EIC-Pathfinder initiative of the European Innovation Council for innovative Quantum technologies.

The project leverages our Spatial Ising Machine (SPIM) device and aims at a new generation of programmable and quantum annealers.

For details, have a look at the HEISINGBERG website.

HEISINGBERG logo and website

See also

Super-Duper Ising Machine by a Single SLM

Photonic extreme learning machine by free-space optical propagation

Photonic brain-inspired platforms are emerging as novel analog computing devices, enabling fast and energy-efficient operations for machine learning. These artificial neural networks generally require tailored optical elements, such as integrated photonic circuits, engineered diffractive layers, nanophotonic materials, or time-delay schemes, which are challenging to train or stabilize. Here we present a neuromorphic photonic scheme – photonic extreme learning machines – that can be implemented simply by using an optical encoder and coherent wave propagation in free space. We realize the concept through spatial light modulation of a laser beam, with the far field that acts as feature mapping space. We experimentally demonstrated learning from data on various classification and regression tasks, achieving accuracies comparable to digital extreme learning machines. Our findings point out an optical machine learning device that is easy-to-train, energetically efficient, scalable and fabrication-constraint free. The scheme can be generalized to a plethora of photonic systems, opening the route to real-time neuromorphic processing of optical data.

arXiv:2105.12123

Photonics Research 9, 1446 (2021)