KEYWORDS: Scattering, Education and training, Gallium nitride, Image restoration, Deep learning, Color imaging, Signal to noise ratio, Light scattering, Biological imaging, Lithium
This paper presents a color computational ghost imaging scheme through a dynamic scattering medium based on deep learning that uses a sole single-pixel detector and is trained by a simulated data set. Due to the color distortion and noise sources being caused by the scattering medium and detector, a simulation data generation method is proposed accordingly that easily adapts to the actual environment. Adequate simulation data sets allow the trained artificial neural networks to exhibit strong reconfiguration capabilities for optical imaging results. It is worth noting that the network trained by our method can reconstruct better details of the image than the simulation data sets according to the ideal state. Its effectiveness is demonstrated in optical imaging experiments with both rotated double-sided frosted glass and a milk solution used as the dynamic scattering medium.
A method is proposed for multiple-image encryption based on optical scanning holography (OSH) using a random phase mask (RPM) and orthogonal compressive sensing (CS). It can destroy the linearity of the traditional OSH system and possess a superior security. On the one hand, images are preferentially preprocessed by utilizing the orthogonal CS to provide a single-layer section for OSH. Therefore, the defocus noise as well as information leakage can be controlled effectively in decryption. On the other hand, each image can be extracted separately without the others’ contents. In addition, the use of RPM can bring about a more ulterior distribution to the cyphertext, which may be better than the case without the RPM. The use of orthogonal modulation matrices and RPM can provide the additional key spaces to guarantee the security of this holography cryptosystem. Simulations and discussions are also made on the cyphertext characteristics as well as the ability of resisting occlusive attack.
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