Document Type

Article

Publication Date

2023

DOI

10.3390/electronics12041039

Publication Title

Electronics

Volume

12

Issue

4

Pages

1039 (1-11)

Abstract

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.

Rights

© 2023 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

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

ORCID

0000-0003-0091-6986 (Li)

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