Facial Thermal Image Translation to RGB Visible Light using GAN

Document Type : Research Paper

Authors

Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran

Abstract

Thermal imaging systems, due to their unique features, such as the ability to record images in different weather conditions, recording images at night, or having anti-counterfeiting properties, have special military, security and judicial applications. However, the images recorded by thermal cameras cannot be recognized by the human eye, and it is very difficult for humans to recognize the faces of thermal images. Converting thermal images to visible light images is in the field of image-to-image content transfer or image to image translation. So far, many deep learning models have been introduced to convert thermal images into visible light. Among these models, adversarial networks have been able to achieve significant progress in this field. In this paper, an attempt was made to improve the ClawGAN network, which is specifically designed to convert thermal images into visible light. Our method is based on the integration of effective techniques such as Unet++, Unet3+, self-attention network in the  generator of the base model. In this way, the network will be able to produce higher quality images that can be recognized by the human eye and have minimal distortion, blur and noise when transferring content from the thermal domain to the visible light domain. The obtained results showed that the proposed generator was able to significantly improve the evaluation criteria such as MSE, PSNR, RMSE, UQI and PSNR-B.

Keywords

Main Subjects

References

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Journal of Modeling in Engineering
Volume 23, Special Issue 81
Celebrating the 50th Anniversary of Semnan University- In Progress
July 2025
Pages 189-198

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History

  • Receive Date: 21 June 2024
  • Revise Date: 05 October 2024
  • Accept Date: 12 November 2024

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