Single Exposure Optical Image Watermarking Using a cGAN Network

• Main Authors: Jiaosheng Li, Yuhui Li, Ju Li, Qinnan Zhang, Guo Yang, Shimei Chen, Chen Wang, Jun Li
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Photonics Journal
• Subjects: Optical image watermarking; image reconstruction; digital holography; deep learning; generative adversarial network
• Online Access: https://ieeexplore.ieee.org/document/9383841/

A single exposure optical image watermarking framework based on deep learning (DL) is proposed in this paper, and original watermark image information can be reconstructed from only single-frame watermarked hologram by using an end-to-end network with high-quality. First, the single exposure watermarked hologram is acquired with our presented phase-shifted interferometry based optical image watermarking (PSOIW) frame, and then all holograms and corresponding watermark images are constructed to the train datasets for the learning of an end-to-end conditional generative adversarial network (cGAN), finally retrieved the watermark image well with the trained cGAN network using only one hologram. This DL-based method greatly reduces the recording or transmitting data burden by 1/4 compared with our presented PSOIW technique, and may provide a new way for the real-time 3D image/video security applications. The feasibility and security of the proposed method are demonstrated by the optical experiment results.