As an important three-dimensional (3D) display technology, holographic 3D display has great application prospects in virtual and augmented reality applications. However, it has been challenging to generate 3D hologram rapidly with high‑reconstruction quality. Here, we proposed a high-speed 3D hologram generation method via convolutional neural network (CNN). The CNN network is trained by unsupervised training, and the trained CNN can generate 3D hologram with 1024×1024 resolution at 100 planes within 60 ms. The feasibility and effectiveness of the proposed method have been demonstrated by simulation. This method will further expand the application of holographic 3D display in remote education, medical treatment, entertainment, and other fields.
In the traditional Fourier single-pixel imaging (FSPI), compressed sampling is often used to improve the acquisition speed. However, the reconstructed image after compressed sampling often has a lower resolution and the quality is difficult to meet the imaging requirements of practical applications. To address this issue, we proposed a novel imaging method that combines deep learning and single-pixel imaging, which can reconstruct high-resolution images with only a small-scale sampling. In the training phase of the network, we attempted to incorporate the physical process of FSPI into the training process. To achieve this objective, a large number of natural images were selected to simulate Fourier single-pixel compressed sampling and reconstruction. The compressed reconstructed samples were subsequently employed for network training. In the testing phase of the network, the compressed reconstruction samples of the test dataset were input into the network for optimization. The experimental results showed that compared with traditional compressed reconstruction methods, this method effectively improved the quality of reconstructed images.
KEYWORDS: 3D acquisition, 3D image processing, 3D displays, Holography, Holograms, Diffraction, Optical scanning systems, Frequency modulation, Fermium, 3D image reconstruction
In recent years, three-dimensional (3D) display technology has developed rapidly, and it is widely used in education, medical, military and other fields. 3D holographic display is regarded as the ultimate solution of 3D display. However, the lack of 3D content is one of the challenges that has been faced by 3D holographic display. The traditional method uses light-field camera and RGB-D camera to obtain 3D information of real scene, which has the problems of high-system complexity and long-time consumption. Here, we proposed a 3D scene acquisition and reconstruction system based on optical axial scanning. First an electrically tunable lens (ETL) was used for high-speed focus shift (up to 2.5 ms). A CCD camera was synchronized with the ETL to acquire multi-focused image sequence of real scene. Then, Tenengrad operator was used to obtain the focusing area of each multi-focused image, and the 3D image were obtained. Finally, the Computer-generated Hologram (CGH) can be obtained by the layer-based diffraction algorithm. The CGH was loaded onto the space light modulator to reconstruct the 3D holographic image. The experimental results verify the feasibility of the system. This method will expand the application of 3D holographic display in the field of education, advertising, entertainment, and other fields.
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