光场技术可以将图像加密从二维提升到三维, 加强加密的安全性。采用重聚焦算法实现图像解密时会引入图像间的干扰。以深度学习技术为框架, 分析图像干扰的规律性, 构造模拟光场数据集, 创建了一个 7层的全卷积神经网络, 以模拟光场数据集作为输入, 原图作为标签, 训练一个全卷积神经网络, 将真实光场解密图像输入得到结果。实验结果表明, 利用全卷积神经网络可以有效改善光场解密图像的干扰问题, 改善解密后的图像质量。

Light field technology can boost image encryption technology from two-dimensional to three-dimensional, and enhance the security of encryption. The refocusing algorithm can be used to achieving image decryption. However, it will introduce interference between images. Based on the deep learning technology, the regularity of image interference is analyzed. The simulated light field data set is constructed. This paper creats a 7-layer full convolutional neural network. As for training the full convolutional neural network, the simulated light field data set is used as input, while the original images are used as labels and input into the full convolutional neural network. Then the real light field decrypted images are input to for testing. The experimental results show that the full convolutional neural network can decrease the interference of the optical field decrypted images obviously and improve the image quality effectively.