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:2019,28(4):199-204
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基于CNN的湍流图像退化强度分类研究
(重庆交通大学 信息科学工程学院, 重庆 400074)
Study on CNN-Based Turbulence Image Degradation Intensity Classification
(School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China)
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投稿时间:2018-10-18    修订日期:2018-11-06
中文摘要: 湍流图像的复原一直是退化图像领域的研究热点,但依据湍流干扰强度对图像进行分类研究相对较少.不同场景的高空航拍图像进行大气湍流处理.调整湍流退化强度值,生成2000张对应的湍流干扰图像,再对这些图像进行预处理后送入卷积神经网络中进行湍流退化强度分类,最后通过优化搭建的卷积神经网络模型的激活函数以及对学习率的调整进一步提升分类准确率.实验表明,卷积神经网络对不同干扰强度的湍流退化图像分类准确率达到80%左右,结果表明该方法对大气湍流退化图像的复原具有一定指导意义.
Abstract:Turbulence image restoration is a hot topic in the field of meteorology, but there are few studies on the classification of turbulent disturbance intensity images. Aerial images of different scenarios are processed for atmospheric turbulence. 2000 corresponding turbulent interference images are generated by adjusting the intensity of turbulence degradation value, and then they are sent into the convolutional neural network after the image preprocessing in turbulent degradation intensity classification, finally by optimizing the structures, convolutional neural network model of the activation function and the adjustment of vector for further improving classification accuracy. Experiments show that the classification accuracy of turbulent degradation images which have different interference intensities by convolutional neural network is about 80%, which demonstrates that this method is of guiding significance for the restoration of atmospheric turbulent degradation images.
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蓝章礼,匡恒,李战,黄涛,曹娟.基于CNN的湍流图像退化强度分类研究.计算机系统应用,2019,28(4):199-204
LAN Zhang-Li,KUANG Heng,LI Zhan,HUANG Tao,CAO Juan.Study on CNN-Based Turbulence Image Degradation Intensity Classification.COMPUTER SYSTEMS APPLICATIONS,2019,28(4):199-204

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