Engineering Internship - Deep Learning for Infrared Thermography Antenna Measurements

ANYFIELDS measures antennas by infrared thermography: a thermosensitive film heated by the radiated field is imaged by an infrared camera, giving a 2D map of the electric field, nonintrusive and very close to the antenna. Two limits remain: the maps are noisy (dynamic range of 25 to 30 dB at best, 35 to 40 dB needed to retrieve side lobes and cross-polarization), and they are blurred by the response of the detector (film and optics), whose resolution is not explained by the theory.

This internship develops physics-driven neural networks, trained without any clean reference, to restore the maps, and compares them rigorously with the conventional methods already implemented.

Benchmark synthetic and measured datasets, reproduction of the conventional methods (wavelet denoising, drift removal, deconvolution, slanted edge).

Estimate PSF of the detector: neural network constrained by the film heat diffusion and the optics, estimated from a few measurements; explain the gap between the theoretical (about 1 mm) and measured (4 to 8 mm) resolution. Compare with conventional methods.

Implement Denoising by self-supervised deep denoising of the maps, quantified gain in dB towards 35 to 40 dB, ranking of the limiting factors of the setup.

Validate by test whether the imaging resolves the individual elements of array antennas in the Ku and Ka bands, before and after improvement. Report on the retained methods.

You will work at the crossroads of electromagnetics