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Alternative TitleInfrared images data augmentation based on generative adversarial networks
陈佛计1,2,3,4; 朱枫1,2,3,4; 吴清潇1,2,3,4; 郝颖明1,2,3,4; 王恩德1,2,3,4
Source Publication计算机应用
Indexed ByCSCD
Funding Organization国家自然科学基金项目 (U1713216)
Keyword红外图像生成 生成对抗网络 图像转换 数据增强 生成图像质量评估


Other Abstract

The great performance of deep learning in many visual tasks largely depends on the improvement of computing power and big data volume. But in many practical projects, it is usually difficult to provide enough data to complete the task. Concern the problem that the infrared data sets are small and the infrared data is hard to collect, a method to generate infrared images based on color images to obtain more infrared image data was proposed. Firstly, the existing color image and infrared image data were employed to construct a paired dataset. Secondly, the generator and the discriminator of the Generative Adversarial Network(GAN)model were formed based on the convolutional neural network and the transposed convolutional neural network. And then, the GAN model was trained based on the paired dataset. Finally, the trained generator was used to transform the color image from the color field to the infrared field. The experimental results show that this method can generate high-quality infrared images and is verified based on quantitative evaluation methods. After the L1 or L2 regularization constraint was added to the loss function, the Fréchet Inception Distance (FID) score was respectively reduced by 23.95, 20.89 on average compared to when not added. As an unsupervised data augmentation method, it can be applied to many other visual tasks that lack train data, such as target recognition, target detection, data imbalance, etc.

Citation statistics
Cited Times:1[CSCD]   [CSCD Record]
Document Type期刊论文
Corresponding Author陈佛计
Recommended Citation
GB/T 7714
陈佛计,朱枫,吴清潇,等. 基于生成对抗网络的红外图像数据增强[J]. 计算机应用,2020,40(7):2084-2088.
APA 陈佛计,朱枫,吴清潇,郝颖明,&王恩德.(2020).基于生成对抗网络的红外图像数据增强.计算机应用,40(7),2084-2088.
MLA 陈佛计,et al."基于生成对抗网络的红外图像数据增强".计算机应用 40.7(2020):2084-2088.
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