We propose a short-path U-net model with average pooling in order to restore in-focus images of multiple transparent particles at ground truth z positions and simultaneously remove their zero-order images, conjugate images, the defocused images of the other particles, and the noise induced by the optical system in a volume. Subsequently, we obtain the lateral location and diameter of each particle in a fast way based on the nature of the particles’ shape; eventually acquire the particle density in the captured volume. The experimental results demonstrate that all sized particles including small– sized and relatively large-sized particles can be restored well by a short-path U-net model, and average pooling performs well when dealing with piece-wise pattern. The proposed scheme can distinguish as many particles as possible in a small volume with small reconstruction depth spacing, such as 50 μm.
We proposed a novel scheme to achieve a computer-generated hologram (CGH). The CGH is generated from a point cloud that is transformed by a mapping relationship of a series of sub-images. The sub-images are converted from elemental images captured by integral imaging pickup system. A more continues depth map can be obtained and a clearer display of the 3D scene can be presented. Moreover, the inherent drawback pseudoscopic problem of integral imaging can also be overcome.
We proposed a fast scheme for computer-generated holography (CGH) to mix 3D scenes. The objects in the proposed include the real and virtual objects. Make a point cloud model of real object, and then converted to a triangular mesh model. And mix the triangular mesh model with virtual 3D object mesh models. Using the angular spectrum method to generated hologram, and it is convenient to accelerating with GPU.
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