GS Iterative Phase Retrieval Algorithm Based on Fusion of Spatial Phase Gradient Descent and Frequency Domain Amplitude Linear Weighting

Hong Cheng, Haonan Zheng, Siwei Sun
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Abstract

For the problems of slow convergence and low accuracy of the traditional linear weighted GS iterative phase retrieval algorithm, a GS iterative phase retrieval algorithm based on the fusion of spatial phase gradient descent and frequency domain amplitude linear weighting is proposed. By zero-padding the image, and then applying phase gradient descent in each iteration of the space domain, the algorithm invokes linear weighting in the frequency domain space, thereby avoiding iterative stagnation while ensuring the convergence speed and improving the accuracy of phase retrieval.
基于空间相位梯度下降和频域幅度线性加权融合的GS迭代相位检索算法
针对传统线性加权GS迭代相位检索算法收敛速度慢、精度低等问题,提出了一种基于空间相位梯度下降和频域幅度线性加权融合的GS迭代相位检索算法。该算法通过对图像进行零填充,然后在空间域的每次迭代中应用相位梯度下降,在频域空间中调用线性加权,在保证收敛速度的同时避免了迭代停滞,提高了相位检索的精度。
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