Chen Chun, Lee Byounghyo, Li Nan-Nan, Chae Minseok, Wang Di, Wang Qiong-Hua, Lee Byoungho
Opt Express. 2021 May 10;29(10):15089-15103. doi: 10.1364/OE.425077.
The stochastic gradient descent (SGD) method is useful in the phase-only hologram optimization process and can achieve a high-quality holographic display. However, for the current SGD solution in multi-depth hologram generation, the optimization time increases dramatically as the number of depth layers of object increases, leading to the SGD method nearly impractical in hologram generation of the complicated three-dimensional object. In this paper, the proposed method uses a complex loss function instead of an amplitude-only loss function in the SGD optimization process. This substitution ensures that the total loss function can be obtained through only one calculation, and the optimization time can be reduced hugely. Moreover, since both the amplitude and phase parts of the object are optimized, the proposed method can obtain a relatively accurate complex amplitude distribution. The defocus blur effect is therefore matched with the result from the complex amplitude reconstruction. Numerical simulations and optical experiments have validated the effectiveness of the proposed method.
随机梯度下降(SGD)方法在仅相位全息图优化过程中很有用,并且可以实现高质量的全息显示。然而,对于当前多深度全息图生成中的SGD解决方案,随着物体深度层数的增加,优化时间会急剧增加,导致SGD方法在复杂三维物体的全息图生成中几乎不实用。在本文中,所提出的方法在SGD优化过程中使用复损失函数而不是仅幅度损失函数。这种替换确保了总损失函数可以仅通过一次计算获得,并且可以大幅减少优化时间。此外,由于物体的幅度和相位部分都得到了优化,所提出的方法可以获得相对准确的复幅度分布。因此,散焦模糊效应与复幅度重建的结果相匹配。数值模拟和光学实验验证了所提出方法的有效性。