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arXiv · 2108.07973

Thermal Image Processing via Physics-Inspired Deep Networks

Vishwanath Saragadam·Akshat Dave·Ashok Veeraraghavan·Richard Baraniuk
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Abstract

We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation. Our key enabling observations are that the images captured by thermal sensors can be factored into slowly changing, scene-independent sensor non-uniformities (that can be accurately modeled using physics) and a scene-specific radiance flux (that is well-represented using a deep network-based regularizer). DeepIR requires neither training data nor periodic ground-truth calibration with a known black body target--making it well suited for practical computer vision tasks. We demonstrate the power of going DeepIR by developing new denoising and super-resolution algorithms that exploit multiple images of the scene captured with camera jitter. Simulated and real data experiments demonstrate that DeepIR can perform high-quality non-uniformity correction with as few as three images, achieving a 10dB PSNR improvement over competing approaches.

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Vishwanath Saragadam, Akshat Dave, Ashok Veeraraghavan, Richard Baraniuk. 2021-08-18. Thermal Image Processing via Physics-Inspired Deep Networks. https://arxiv.org/abs/2108.07973

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