| 基于全卷积网络的红外图像非均匀性校正算法 |
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| 引用本文： | 牟新刚,崔健,周晓. 基于全卷积网络的红外图像非均匀性校正算法[J]. 红外技术, 2022, 44(1): 21-27 |
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| 作者姓名： | 牟新刚 崔健 周晓 |
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| 作者单位： | 武汉理工大学 机电工程学院，湖北 武汉 430070 |
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| 基金项目： | 国家自然科学基金项目(61701357);中央高校基本科研业务费专项资金资助(183204007)。 |
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| 摘 要： | 针对红外成像系统在经过两点校正后,随时间漂移仍然会出现的非均匀性噪声,提出一种基于全卷积深度学习网络的红外图像非均匀性校正算法,使用子网络与主网络相结合的方式进行非均匀性校正.该算法设计了非均匀性等级估计子网络,将含有非均匀性噪声的红外图像输入子网络后,输出非均匀性等级估计图,并和待校正红外图像一并输入校正主网络.子网...
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| 关 键 词： | 非均匀性等级估计子网络 红外图像 非均匀性校正 深度学习 |
| 收稿时间： | 2021-02-08 |
Infrared Image Non-uniformity Correction Algorithm Based on Full Convolutional Network |
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| MOU Xingang,CUI Jian,ZHOU Xiao. Infrared Image Non-uniformity Correction Algorithm Based on Full Convolutional Network[J]. Infrared Technology, 2022, 44(1): 21-27 |
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| Authors: | MOU Xingang CUI Jian ZHOU Xiao |
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| Affiliation: | School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China |
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| Abstract: | The infrared imaging system will still exhibit non-uniform noise after two-point correction. An infrared image non-uniformity correction algorithm based on a fully convolutional deep learning network was proposed in response to this problem. This algorithm combines the subnetwork and main network for non-uniformity correction. The network contains a nonuniformity-level estimation subnetwork. After inputting the infrared image with non-uniform noise into the non-uniformity level estimation subnetwork, the outputted non-uniformity level estimation map is input into the main network together with the original noise infrared image. The non-uniformity level estimated map generated by the subnetwork prevents the network from overfitting only for the non-uniformity of the same grade. After experimental verification, the algorithm overcomes the problem of edge blur generated by the scene-based algorithm. The algorithm will not appear blurred, the images have high definition and rich details, and the quality of images is good. |
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| Keywords: | non-uniformity level estimation subnetwork infrared image non-uniformity correction deep learning |
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