We present a simple and fast calculation algorithm for a computer-generated hologram (CGH) by use of wavefront recording plane. The wavefront recording plane is placed between the object data and a CGH. When the wavefront recording plane is placed close to the object, the object light passes through a small region on the wave recording plane. The computational complexity for the object light is very small. We can obtain a CGH to execute diffraction calculation from the wavefront recording plane to the CGH. The computational complexity is constant. The total computational complexity is dramatically reduced in comparison with conventional CGH calculations.

A rapid calculation method of Fresnel computer-generated-hologram (CGH) using look-up table and wavefront-recording plane (WRP) methods toward three-dimensional (3D) display is presented. The method consists of two steps: the first step is the calculation of a WRP that is placed between a 3D object and a CGH. In the second step, we obtain an amplitude-type or phase-type CGH to execute diffraction calculation from the WRP to the CGH. The first step of the previous WRP method was difficult to calculate in real-time due to the calculation cost. In this paper, in order to obtain greater acceleration, we apply a look-up table method to the first step. In addition, we use a graphics processing unit in the second step. The total computational complexity is dramatically reduced in comparison with conventional CGH calculations. We show optical reconstructions from a 2,048 × 2,048 phase-type CGH generated by about 3 × 10⁴ object points over 10 frames per second.

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In this paper, we report fast calculation of a computer-generated-hologram using a new architecture of the HD5000 series GPU (RV870) made by AMD and its new software development environment, OpenCL. Using a RV870 GPU and OpenCL, we can calculate 1,920×1,024 resolution of a CGH from a 3D object consisting of 1,024 points in 30 milli-seconds. The calculation speed realizes a speed approximately two times faster than that of a GPU made by NVIDIA.

Deep learning has been developing rapidly, and many holographic applications have been investigated using deep learning. They have shown that deep learning can outperform previous physically-based calculations using lightwave simulation and signal processing. This review focuses on computational holography, including computer-generated holograms, holographic displays, and digital holography, using deep learning. We also discuss our personal views on the promise, limitations and future potential of deep learning in computational holography.