@inproceedings{e77cd6dcb950497fb9692d03fd625d95,
title = "HGGAN: Visible to Thermal Translation Generative Adversarial Network Guided by Heatmap",
abstract = "The realization of multi-modal image fusion requires sufficient cross domain data. Translating the visible images is an effective method to obtain thermal-visible paired images from the visible image domain to the thermal image domain. The current translation methods have some disadvantages, such as unreasonable distribution of thermal radiation intensity, blurred edges, spatial distortion and feature loss. So they are not friendly to downstream tasks. Based on the generation and reconstruction strategy of CycleGAN, we propose an image to image translation network guided by heatmap which is called HGGAN. We use the heatmap of object that detected by network to encode the heatmap code, and combine the image edge code to improve the image generation performance. We test the image standard of the generated image, and use the object detection network to verify.",
keywords = "cross domain, generative adversarial network, heatmap, image translation, thermal image",
author = "Tong Liu and Yufeng Liu and Wenda Xu and Yuandong Pu and Yuqi Hao and Wei Zuo",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Unmanned Systems, ICUS 2022 ; Conference date: 28-10-2022 Through 30-10-2022",
year = "2022",
doi = "10.1109/ICUS55513.2022.9986689",
language = "English",
series = "Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "171--176",
editor = "Rong Song",
booktitle = "Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022",
address = "United States",
}