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

作者：Liying Lang, Jia-Lei Lu, Nana Yu, Sixing Xi, Xue-Guang Wang, Lei Zhang, Xiaoxue Jiao · 发表于：Acta Physica Sinica · 年份：2020 · DOI：10.7498/aps.69.20200805 · 被引用次数：4 · 研究领域：Chaos-based Image/Signal Encryption、Advanced Steganography and Watermarking Techniques、Image and Video Stabilization

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 ...