Single infrared image enhancement using a deep convolutional neural network

•We propose a conditional GAN-based network to address the traditional infrared image enhancement issue.•We design a refined generative sub-network to achieve the best results in enhancing performance and range of application.•Extensive experiments show that our IE-CGAN method is competitive with ex...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2019-03, Vol.332, p.119-128
Main Authors: Kuang, Xiaodong, Sui, Xiubao, Liu, Yuan, Chen, Qian, Gu, Guohua
Format: Article
Language:English
Subjects:
Deep learning
Encoder–decoder network
Enhancement
Generative adversarial network
Infrared images
Residual network
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Summary:•We propose a conditional GAN-based network to address the traditional infrared image enhancement issue.•We design a refined generative sub-network to achieve the best results in enhancing performance and range of application.•Extensive experiments show that our IE-CGAN method is competitive with existing infrared image enhancement approaches. In this paper, we propose a deep learning method for single infrared image enhancement. A fully convolutional neural network (CNN) is used to produce images with enhanced contrast and details. The conditional generative adversarial networks are incorporated into the optimization framework to avoid the background noise being amplified and further enhance the contrast and details. The existing convolutional neural network architectures, such as residual architectures and encoder–decoder architectures, fail to achieve the best results both in terms of network performance and application scope for infrared image enhancement task. To address this problem, we specifically design a new refined convolutional neural architecture that produces visually very appealing results with higher contrast and sharper details compared to other network architectures. Visible images are used for training since there are fewer infrared images. Proper training samples are generated to ensure that the network trained on visible images can be well applied to infrared images. Experiments demonstrate that our approach outperforms existing image enhancement algorithms in terms of contrast and detail enhancement. Code is available at https://github.com/Kuangxd/IE-CGAN.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2018.11.081