Acta Optica Sinica, Volume. 40, Issue 18, 1810001(2020)
Infrared Simulation Based on Cascade Multi-Scale Information Fusion Adversarial Network
In this paper, we propose a cascade multi-scale information fusion generative adversarial network (CMIF-GAN) for infrared image simulation, which can estimate the infrared map from a visible image. Inspired by the connections and differences between visible and infrared features, CMIF-GAN adopts a cascaded structure composed of two levels of adversarial networks. With a large overall receptive field, the first-level adversarial network focuses on reconstructing structural information of the infrared image, and adds a semantic segmentation image task as auxiliary information. To enrich detailed texture information of the infrared image, the second-level adversarial network uses the grayscale inverted visible (GIV) images as auxiliary information and adopts a small overall receptive field network. Otherwise, the second-level adversarial network can integrate the multiple receptive information by a multi-scale fusion module (MFM) to improve algorithm accuracy. Experiments on public dataset demonstrate that CMIF-GAN can efficiently translate visible images to corresponding infrared images, and outperform previous methods in objective metrics and subjective vision.
Get Citation
Copy Citation Text
Ruiming Jia, Tong Li, Shengjie Liu, Jiali Cui, Fei Yuan. Infrared Simulation Based on Cascade Multi-Scale Information Fusion Adversarial Network[J]. Acta Optica Sinica, 2020, 40(18): 1810001
Category: Image Processing
Received: Apr. 7, 2020
Accepted: Jun. 3, 2020
Published Online: Aug. 27, 2020
The Author Email: Jia Ruiming (jiaruiming@ncut.edu.cn)