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计算机系统应用:2018,27(9):170-175
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基于深度卷积神经网络的图像重建算法
于波1,2, 方业全1,2, 刘闽3, 董君陶4
(1.中国科学院大学, 北京 100049;2.中国科学院 沈阳计算技术研究所, 沈阳 110168;3.沈阳市环境检测中心站, 沈阳 110000;4.沈阳市第二十七中学, 沈阳 110011)
Image Reconstruction Algorithm Based on Deep Convolution Neural Network
YU Bo1,2, FANG Ye-Quan1,2, LIU Min3, DONG Jun-Tao4
(1.University of Chinese Academy of Sciences, Beijing 100049, China;2.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;3.Shenyang Environmental Monitoring Center Station, Shenyang 110000, China;4.Shenyang Twenty-Seventh Middle School, Shenyang 110011, China)
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投稿时间:2018-02-01    修订日期:2018-02-28
中文摘要: 视频或者图像在传输过程中,可能出现随机性的误码、突发性的误码、传输中的丢包等等,对解码出的图像数据也会有严重的影响.本文提出了基于深度学习的图像重建算法:一种基于图像背景预测生成模糊区域内容的无监督图像重建神经网络模型.为了重建出逼真的图像,神经网络模型需要既理解整个图像的内容,又为缺失的部分重构出一个合理的假设.损失函数包含标准像素级重建损失和对抗损失,在训练卷积神经网络模型时,能够更好地处理图像中的结构细节产生更清晰的结果.通过实验可以发现本文设计的深度卷积神经网络模型与基于样本插值的算法相比在图像重建中有着较好的效果.
Abstract:In the process of video or image transmission, there may be random error, sudden error, packet loss, and so on, which will also have a serious impact on the decoded image data. This paper presents an image reconstruction algorithm based on depth learning:an unsupervised image reconstruction neural network model based on image background prediction to generate fuzzy region content. In order to reconstruct a vivid image, a neural network model not only needs to understand the content of the image, but also to reconstruct the missing part of a reasonable assumption. The loss function includes standard pixel level reconstruction loss and counterwork loss. When training the convolution neural network model, the loss function can better deal with the structure details in the image and produce clearer results. Through experiments, we can find that the neural network model of depth convolution designed in this study has better effect in image reconstruction than the algorithm based on sample interpolation.
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于波,方业全,刘闽,董君陶.基于深度卷积神经网络的图像重建算法.计算机系统应用,2018,27(9):170-175
YU Bo,FANG Ye-Quan,LIU Min,DONG Jun-Tao.Image Reconstruction Algorithm Based on Deep Convolution Neural Network.COMPUTER SYSTEMS APPLICATIONS,2018,27(9):170-175

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