The secret image can be recovered when identical illumination patterns are projected onto multiple visual key images and a single detector is used to record the total light intensities.
Despite the success, the limitations and drawbacks of deep learning in optical imaging have been seldom investigated.
In many previous works, a single-pixel imaging (SPI) system is constructed as an optical image encryption system.
Like an optical computer, the system can perform machine learning tasks such as number digit recognition in an all-optical manner.
In our proposed scheme, multiple input images can be simultaneously fed to an optical diffractive neural network (DNN) system and each corresponding output image will be displayed in a non-overlap sub-region in the output imaging plane.
It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format.
Single-pixel imaging (SPI) has a major drawback that many sequential illuminations are required for capturing one single image with long acquisition time.
A high quality object image can only be computationally reconstructed after a large number of illuminations, with disadvantages of long imaging time and high cost.
Information security is a critical issue in modern society and image watermarking can effectively prevent unauthorized information access.