Optical scanning cryptography (OSC) has garnered considerable attention because of its ability to acquire an incoherent hologram from a physical object using a single-pixel sensor. In this paper, we generate a new type of scanning pattern, named light spring (LS), using the two-pupil optical heterodyne scanning system. The light spring, which is characterized by helical structures for their phase and intensity profiles, can be built on the superposition of two distinct laser frequency components. An optical scanning cryptography scheme based on LS is proposed. The scheme expands the temporal characteristics of orbital angular momentum (OAM) multiplexing and improves the encryption capability, key space and security of the system.
Optical scanning cryptography (OSC) is an optical image encryption method encrypting information incoherently based on two-pupil heterodyne scanning optical system. But numerical reconstruction of a 3-D volumetric image from an optical scanned hologram is a difficult task. The main problems are the intensive computational load, and the heavy blurring of each reconstructed section with the defocused noise from other sections.In this talk, we propose a deep-learning based reconstruction algorithm in optical scanning holography, which can generate reconstruction images in high quality. DNNs are created based on the U-net structure to learn the mapping between holograms and reconstruction images. Simulation and experimental results showed that the deep-learning based method is able to reconstruct the optical scanning hologram in real time for the removal of defocus noise.
In this research, we propose a deep-learning-based computer generated hologram generation algorithm. The algorithm is able to generate a de-noised off-axis computer generated hologram. A de-noising convolutional neural network (DnCnn) is trained with added non-Gaussian and non-stationary speckle noise. Signal noise ratio (SNR), peak signal to noise ratio (PSNR), and mean square error (MSE) are used to evaluate the performance of the DnCnn. What’s more, compared with the reconstructed image, the pixel distribution of the denoising image is closer to the original image. Results show that the algorithm is superior to conventional algorithms improvement about the quality of the reconstructed images.
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