An unsupervised colorization method of thermal infrared image based on edge consistency

Jiaming Cai; Xin Tang; Yao Hu; Shaohui Zhang

doi:10.1117/12.3004945

9 October 2023 An unsupervised colorization method of thermal infrared image based on edge consistency

Jiaming Cai, Xin Tang, Yao Hu, Shaohui Zhang

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127911A (2023) https://doi.org/10.1117/12.3004945
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

Due to the lack of pixel level structural matching data, thermal infrared grayscale images are more difficult to color than visible and near-infrared grayscale images. Therefore, this paper proposes a unsupervised learning method based on CycleGAN. On the basis of CycleGAN, a pre trained edge monitor is introduced to calculate the edge feature map before and after image transformation, and the edge similarity loss function is calculated as the basis for optimizing the neural network parameters. The experimental results show that the proposed method effectively reduces the loss of effective edge information during the coloring process and suppresses the generation of abnormal edge information during the coloring process.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiaming Cai, Xin Tang, Yao Hu, and Shaohui Zhang "An unsupervised colorization method of thermal infrared image based on edge consistency", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127911A (9 October 2023); https://doi.org/10.1117/12.3004945

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