Perspectives and limitations of visible-thermal image pair synthesis via generative adversarial networks

Danick Panchard; François Marelli; Edouard De Moura Presa; Peter Wellig; Michael Liebling

doi:10.1117/12.2599889

12 September 2021 Perspectives and limitations of visible-thermal image pair synthesis via generative adversarial networks

Danick Panchard, François Marelli, Edouard De Moura Presa, Peter Wellig, Michael Liebling

Author Affiliations +

Proceedings Volume 11865, Target and Background Signatures VII; 1186509 (2021) https://doi.org/10.1117/12.2599889
Event: SPIE Security + Defence, 2021, Online Only

Abstract

Many applications rely on thermal imagers to complement or replace visible light sensors in difficult imaging conditions. Recent advances in machine learning have opened the possibility of analyzing or enhancing images, yet these methods require large annotated databases. Training approaches that leverage data augmentation via simulated and synthetically-generated images could offer promising prospects. Here, we report on a method that uses generative adversarial nets (GANs) to synthesize images of a complementary contrast. Starting from a dual-modality dataset of co-registered visible and thermal images, we trained a GAN to generate synthetic thermal images from visible images and vice versa. Our results show that the procedure yields sharp synthesized images that might be used to augment dual-modality datasets or assist in visual interpretation, yet are also subject to the limitations imposed by contrast independence between thermal and visible images.

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Danick Panchard, François Marelli, Edouard De Moura Presa, Peter Wellig, and Michael Liebling "Perspectives and limitations of visible-thermal image pair synthesis via generative adversarial networks", Proc. SPIE 11865, Target and Background Signatures VII, 1186509 (12 September 2021); https://doi.org/10.1117/12.2599889

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