HGGAN: Visible to Thermal Translation Generative Adversarial Network Guided by Heatmap
Tong Liu, Yufeng Liu, Wenda Xu, Yuandong Pu, Yuqi Hao, Wei Zuo · 2022 IEEE International Conference on Unmanned Systems (ICUS) · 2022
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.