Unsupervised image-to-image translation techniques have been used in many applications, including visible-to-Long-Wave Infrared (visible-to-LWIR) image translation, but very few papers have explored visible-to-Mid-Wave Infrared (visible-to-MWIR) image translation. In this paper, we investigated unsupervised visible-to-MWIR image translation using generative adversarial networks (GANs). We proposed a new model named MWIRGAN for visible-to-MWIR image translation in a fully unsupervised manner. We utilized a perceptual loss to leverage shape identification and location changes of the objects in the translation. The experimental results showed that MWIRGAN was capable of visible-to-MWIR image translation while preserving the object’s shape with proper enhancement in the translated images and outperformed several competing state-of-the-art models. In addition, we customized the proposed model to convert game-engine-generated (a commercial software) images to MWIR images. The quantitative results showed that our proposed method could effectively generate MWIR images from game-engine-generated images, greatly benefiting MWIR data augmentation.
© 2023 by the authors.
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Article states: The ATR dataset is available on https://dsiac.org/databases/atralgorithm-development-image-database/, accessed on 1 January 2020.
Original Publication Citation
Uddin, M. S., Kwan, C., & Li, J. (2023). MWIRGAN: Unsupervised visible-to-MWIR image translation with generative adversarial network. Electronics, 12(4), 1-11, Article 1039. https://doi.org/10.3390/electronics12041039
Uddin, Mohammad Shahab; Kwan, Chiman; and Li, Jiang, "MWIRGAN: Unsupervised Visible-to MWIR Image Translation With Generative Adversarial Network" (2023). Electrical & Computer Engineering Faculty Publications. 354.
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