Optimization of phase-only holograms calculated with scaled diffraction calculation through deep neural networks
Optimization of phase-only holograms calculated with scaled diffraction calculation through deep neural networks
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通过深度神经网络通过缩放衍射计算计算纯相位全息图的优化
DOI：
10.1007/s00340-022-07753-7
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
and Tomoyoshi Ito
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文献类型：
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作者：
Yoshiyuki Ishii;Tomoyoshi Shimobaba;David Blinder;Tobias Birnbaum;Peter Schelkens;Takashi Kakue;and Tomoyoshi Ito
计算机生成全息图（CGH）用于全息三维（3D）显示和全息投影。使用纯相位CGH的重建图像的质量降低，因为重建图像的幅度难以控制。诸如Gerchberg-Saxton（GS）算法的迭代优化方法是用于改善图像质量的一种选择。他们以迭代的方式优化CGH，以获得更高的图像质量。然而，这样的迭代计算是耗时的，并且图像质量的改善常常是停滞的。最近，已经提出了基于深度学习的全息图计算。深度神经网络直接从输入图像数据中推断CGH。然而，它仅限于重建与全息图大小相同的图像。在这项研究中，我们使用深度学习来优化使用缩放衍射计算和随机无相方法生成的纯相位CGH。通过将随机无相位方法与缩放衍射计算相结合，可以处理比全息图大的可缩放再现像。与GS算法相比，该方法在高质量和速度方面都得到了优化。
Computer-generated holograms (CGHs) are used in holographic three-dimensional (3D) displays and holographic projections. The quality of the reconstructed images using phase-only CGHs is degraded because the amplitude of the reconstructed image is difficult to control. Iterative optimization methods such as the Gerchberg–Saxton (GS) algorithm are one option for improving image quality. They optimize CGHs in an iterative fashion to obtain a higher image quality. However, such iterative computation is time-consuming, and the improvement in image quality is often stagnant. Recently, deep learning-based hologram computation has been proposed. Deep neural networks directly infer CGHs from input image data. However, it is limited to reconstructing images that are the same size as the hologram. In this study, we use deep learning to optimize phase-only CGHs generated using scaled diffraction computations and the random phase-free method. By combining the random phase-free method with the scaled diffraction computation, it is possible to handle a zoomable reconstructed image larger than the hologram. In comparison to the GS algorithm, the proposed method optimizes both high quality and speed.