Wu Yang, Wang Jun, Chen Chun, Liu Chan-Juan, Jin Feng-Ming, Chen Ni
Opt Express. 2021 Jan 18;29(2):1412-1427. doi: 10.1364/OE.413723.
In the conventional weighted Gerchberg-Saxton (GS) algorithm, the feedback is used to accelerate the convergence. However, it will lead to the iteration divergence. To solve this issue, an adaptive weighted GS algorithm is proposed in this paper. By replacing the conventional feedback with our designed feedback, the convergence can be ensured in the proposed method. Compared with the traditional GS iteration method, the proposed method improves the peak signal-noise ratio of the reconstructed image with 4.8 dB on average. Moreover, an approximate quadratic phase is proposed to suppress the artifacts in optical reconstruction. Therefore, a high-quality image can be reconstructed without the artifacts in our designed Argument Reality device. Both numerical simulations and optical experiments have validated the effectiveness of the proposed method.
在传统的加权格尔奇贝格 - 萨克斯顿（GS）算法中，反馈用于加速收敛。然而，这会导致迭代发散。为了解决这个问题，本文提出了一种自适应加权GS算法。通过用我们设计的反馈取代传统反馈，该方法可以确保收敛。与传统的GS迭代方法相比，该方法使重建图像的峰值信噪比平均提高了4.8 dB。此外，还提出了一种近似二次相位来抑制光学重建中的伪像。因此，在我们设计的论证现实设备中可以重建出没有伪像的高质量图像。数值模拟和光学实验都验证了该方法的有效性。
