Advanced Deep Learning Techniques for High-Quality Synthetic Thermal Image Generation

Vicente Pavez, Gabriel Hermosilla, Manuel Silva, Gonzalo Farias

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we introduce a cutting-edge system that leverages state-of-the-art deep learning methodologies to generate high-quality synthetic thermal face images. Our unique approach integrates a thermally fine-tuned Stable Diffusion Model with a Vision Transformer (ViT) classifier, augmented by a Prompt Designer and Prompt Database for precise image generation control. Through rigorous testing across various scenarios, the system demonstrates its capability in producing accurate and superior-quality thermal images. A key contribution of our work is the development of a synthetic thermal face image database, offering practical utility for training thermal detection models. The efficacy of our synthetic images was validated using a facial detection model, achieving results comparable to real thermal face images. Specifically, a detector fine-tuned with real thermal images achieved a 97% accuracy rate when tested with our synthetic images, while a detector trained exclusively on our synthetic data achieved an accuracy of 98%. This research marks a significant advancement in thermal image synthesis, paving the way for its broader application in diverse real-world scenarios.

Original languageEnglish
Article number4446
JournalMathematics
Volume11
Issue number21
DOIs
StatePublished - Nov 2023

Keywords

  • deep learning
  • face detection
  • generative models
  • thermal imaging

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