基于深度学习的联合变换相关器光学图像加密系统去噪方法  

In depth learning based method of denoising joint transform correlator optical image encryption system

作　　者：郎利影 陆佳磊 于娜娜 席思星 王雪光[2] 张雷[2] 焦小雪[2] Lang Li-Ying;Lu Jia-Lei;Yu Na-Na;Xi Si-Xing;Wang Xue-Guang;Zhang Lei;Jiao Xiao-Xue(Advanced Laser Technology Research Center,Hebei University of Technology,Tianjin 300401,China;Hebei University of Engineering,Handan 056038,China)

机构地区：[1]河北工业大学先进激光技术研究中心,天津300401 [2]河北工程大学,邯郸056038

出　　处：《物理学报》2020年第24期155-162,共8页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:11904073);河北省自然科学基金(批准号:F2019402351);河北省教育厅青年拔尖人才项目(批准号:BJ2020028);河北省科技计划(批准号:20371802D)资助课题.

摘　　要：提出了一种基于深度学习的联合变换相关器(JTC)光学图像加密系统新型去噪方法.针对JTC光学图像加密系统中解密图像噪声的问题,设计了一种基于生成对抗网络的去噪框架,并使用密集模块加强特征信息复用,提高了网络的性能.该方法通过引入通道注意力机制使网络区分不同通道的权重,学习各通道之间的关联,使网络能选择性的加强有用特征信息并抑制无用特征信息;在损失函数方面,加入非对抗损失部分,结合对抗损失和生成器模型提高了解密噪声图像中高频信息的恢复质量;最后重建出高质量的解密图像.将该方法用于传统的JTC光学图像加密系统,数值计算和模拟实验结果表明,该方法可极大地消除JTC光学图像加密系统中噪声影响,有效地提高JTC光学图像加密系统用于高质量图像加密的有效性和可行性.There is serious noise interference in the decryption process of the joint transform correlator(JTC)optical encryption system,so the quality of the decrypted image cannot meet the accuracy requirements in most cases.The quality of decrypted image can be improved to a certain extent when the phase key is designed by the Gerchberg-Saxton algorithm and the iterative algorithm fuzzy control algorithm,but the complexity of the design process is inevitable and the quality of the decrypted image still needs improving.Recently,the in depth learning technology has attracted the attention of scholars in the fields of computer vision,natural language processing and optical information processing.In order to deal with the noise interference in the JTC optical encryption system,combining the current deep learning method,in this paper we propose a new denoising method for JTC optical image encryption system based on in depth learning,the dense modules are added into the generated network to enhance the reuse of feature information and improve the performance of the network.The latest self-attention mechanism area is added into the network to distinguish the weights of different channels and learn the relationship between channel and channel,so that the network can selectively strengthen the useful feature information but suppress useless feature information.The density module and the channel attention module are integrated into a DCAB synthesis module,which can effectively extract the image feature information and improve the performance of the network.The receptive field of the convolution kernel is enlarged by two down-sampling and the feature map is restored to its original size by two up-sampling.The VGG-19 is used to extract high-frequency details and texture features of images,meanwhile,the non-adversarial loss and mean-square error(MSE)loss are added into the loss function to reduce the difference among the image samples.The quality of noise-reduced images in this method are obviously better than that of the existing de

关 键 词：光学图像加密 深度学习 信息安全 神经网络 联合变换相关器 

分 类 号：TP309.7[自动化与计算机技术—计算机系统结构] TP18[自动化与计算机技术—计算机科学与技术]