Edge focused super-resolution of thermal images

Yannick Zoetgnande (1) , Jean-Louis Dillenseger (1) , Javad Alirezaie (2)

1 LTSI - Laboratoire Traitement du Signal et de l'Image
2 Department of Electrical and Computer Engineering [Toronto, Canada]

In this work, a super-resolution method is proposed for indoor scenes captured by low-resolution thermal cameras. The proposed method is called Edge Focused Thermal Super-resolution (EFTS) which contains an edge extraction module enforcing the neural networks to focus on the edge of images. Utilizing edge information, our model, based on residual dense blocks, can perform super-resolution for thermal images, while enhancing the visual information of the edges. Experiments on benchmark datasets showed that our EFTS method achieves better performance in comparison to the state-of-the-art techniques.

Mots clés: Thermal image; Neural network; Super-resolution; Edge extraction

Domaines: Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]

Yannick Zoetgnande, Jean-Louis Dillenseger, Javad Alirezaie. Edge focused super-resolution of thermal images. International Joint Conference on Neural Networks, Jul 2019, Budapest, Hungary. pp.1-8, ⟨10.1109/IJCNN.2019.8852320⟩. ⟨hal-02270646⟩