DDGANA: dual-discriminator GAN with attention module for infrared and visible image fusion

Jinwen Ren; Jingjing Wang; Hongzhen Li; Zengzhao Sun; Zhenye Luan; Zishu Yu; Huaqiang Xu; Qing Hua

doi:10.1117/12.2611730

11 November 2021 DDGANA: dual-discriminator GAN with attention module for infrared and visible image fusion

Jinwen Ren, Jingjing Wang, Hongzhen Li, Zengzhao Sun, Zhenye Luan, Zishu Yu, Huaqiang Xu, Qing Hua

Proceedings Volume 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence; 120760E (2021) https://doi.org/10.1117/12.2611730
Event: Fourth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2021), 2021, Shanghai, China

Abstract

Infrared images can be differentiated between target and background according to the distinction in thermal radiation, which generally works under any lighting condition. In comparison, visible images available in abundant detail, consistent as human visual system. In this study, a novel approach for the fusion of infrared and visible images is proposed built on a dual-discriminator GAN with attention module (DDGANA). Our approach establishes confrontation training between one generator and two discriminators. The goal of the generator is to output result with contrast information and details. The two discriminants aim to increase the similarity between the images generated by the generator and the infrared and visible images. After sequential adversarial training, DDGANA outputs images that have preserved the high contrast and the abundant texture detail. Experiments on the TNO dataset prove that our approach obtains an improved performance over the other approaches.

Citation Download Citation

Jinwen Ren, Jingjing Wang, Hongzhen Li, Zengzhao Sun, Zhenye Luan, Zishu Yu, Huaqiang Xu, and Qing Hua "DDGANA: dual-discriminator GAN with attention module for infrared and visible image fusion", Proc. SPIE 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence, 120760E (11 November 2021); https://doi.org/10.1117/12.2611730

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KEYWORDS

Image fusion

Infrared imaging

Infrared radiation

Visible radiation

Gallium nitride

Convolution

Image processing

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