High-Quality Object Reconstruction From One-Dimensional Compressed Encrypted Signal Based on Multi-Network Mixed Learning
Yuhui Li, Jiaosheng Li, Jun Li · IEEE Access · 2020
Conventional optical image encryption methods based on phase-shifting interferometry need at least two interferograms, and the storage or transmission of interferograms needs to occupy a lot of resources. At the same time, the low quality of reconstructed complex natural images has always been a main limiting factor in the application of optical image security. In this paper, a high-quality object reconstruction method from one-dimensional compressed encrypted signal based on multi-network mixed learning is proposed. First, an encrypted interferogram can be obtained using the double random phase encoding (DRPE) method. Then, we can obtain the one-dimensional compressed sampling signal of the encrypted hologram on the photodiode using single-pixel compressive holographic imaging method. Finally, the mapping of 1D signal to 2D object image can be learned utilizing multiple neural network models. Numerical simulation results show that the complex natural images can be reconstructed using the proposed method with high quality at lower sampling ratio.