论文标题
基于深度学习的光学图像隐藏
Deep-learning-based optical image hiding
论文作者
论文摘要
本文提出了一个基于深度学习（DL）的光学图像隐藏的新颖框架，并且可以通过使用高质量的端到端网络从干涉图中重建隐藏信息。通过使用隐藏图像和对象图像之间的先前数据，对生成对抗网络进行了训练，以便它可以学习隐藏模型，从而导致只需要传输并记录干涉图以重建图像。此外，可以在没有光学逆衍射中参数的情况下获得重建过程，并且重建结果不会受到相移偏差和噪声的影响，这对于实际应用很方便。通过光学实验结果证明了所提出方法的可行性和安全性。
A novel framework of optical image hiding based on deep learning (DL) is proposed in this paper, and hidden information can be reconstructed from an interferogram by using an end to end network with high-quality. By using the prior data between the hidden image and the object image, a generative adversarial network was trained so that it can learn the hiding model, which resulting in only an interferogram needs to be transmitted and recorded to reconstruct image. Moreover, reconstruction process can be obtained without the parameters in optical inverse diffraction and the reconstruction result will not be affected by the phase shifts deviation and noise, which is convenient for practical application. The feasibility and security of the proposed method are demonstrated by the optical experiment results.
