Engineering Internship - Deep Learning for Infrared Thermography Antenna Measurements

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THE POSITION 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.
Missions :
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, imaging and deep learning, with the engineers of the Innovation team.
Location: Toulouse (city centre)
Start: As soon as possible
Duration: 6 months
Profile Bac+5 - Engineering school / Master in applied mathematics, signal and image processing, machine learning and/or electromagnetics

Programming skills (preferably Python), experience with a deep learning framework

Knowledge of inverse problems or infrared cameras is a plus

Team spirit, proactive attitude and good communication skills in an interdisciplinary team

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职位

ANYFIELDS通过红外热成像技术测量天线：由辐射场加热的热敏薄膜由红外相机成像，从而获得电场二维分布图，该方法非侵入式且非常接近天线。但仍存在两个限制：图像噪声较大（动态范围最好为25至30 dB，而提取旁瓣和交叉极化需要35至40 dB），并且图像因探测器响应（薄膜和光学系统）而模糊，其分辨率无法用理论解释。

本实习旨在开发物理驱动的神经网络，在无需任何干净参考数据的情况下训练以恢复图像，并将其与已实现的传统方法进行严格比较。

任务：

对合成数据集和实测数据集进行基准测试，复现传统方法（小波去噪、漂移去除、反卷积、斜边法）。

估计探测器的PSF：由薄膜热扩散和光学系统约束的神经网络，从少量测量中估计；解释理论分辨率（约1 mm）与实测分辨率（4至8 mm）之间的差距。与传统方法进行比较。

实现图像的自监督深度去噪，量化以dB计的增益向35至40 dB迈进，并对装置的限制因素进行排序。

通过测试验证成像是否能在改进前后分辨Ku和Ka波段的阵列天线各单元。报告所采用的方法。

您将在电磁学、成像和深度学习的交叉领域工作，与创新团队的工程师合作。
地点：图卢兹（市中心）
开始时间：尽快
时长：6个月
要求：硕士五年级（Bac+5）——工程师学校/应用数学、信号与图像处理、机器学习和/或电磁学硕士

编程能力（ preferably Python），具有深度学习框架经验

了解逆问题或红外相机者优先

具有团队精神、积极主动的态度，以及在跨学科团队中良好的沟通能力

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