在传统多波长迭代算法的基础上, 引入角谱传输理论和梯度加速函数, 提出一种快速收敛的相位恢复迭代算法--多波长梯度加速迭代算法。该算法利用不同波长的光源经过相同光路在固定位置处所探测的光场强度信息, 通过迭代逼近, 并在迭代过程中引入梯度加速方法, 提高收敛速度, 从而恢复出输入面的相位信息。构建仿真模型, 随机选取初始迭代值, 对已知相位分布的输入场进行相位恢复, 并与传统多波长迭代算法进行了比较, 该算法表征相位面复原精度的相对均方根值均达到10-3数量级, 收敛速率提高了两倍以上。在635~785 nm波段内选取多组不同波长数量的入射光利用该算法进行对比实验, 结果表明当波长数量为7、 8时该算法具有良好快速的收敛特性及高精度的相位恢复能力。

Based on the traditional multi-wavelength iterative algorithm, we introduce the angular spectrum transmission theory and the gradient acceleration function, and present a fast convergent phase restoration iterative algorithm, namely, the multi-wavelength gradient acceleration phase retrieval iterative algorithm. The algorithm uses the light field intensity information detected at a fixed position when the light sources with different wavelengths go through the same light path. Through iterative approximation and by introducing the gradient acceleration function in the iterative process, the convergence speed is improved, and the input phase information is recovered. The simulation model is built, the initial iteration value is selected randomly, the input field for a known phase distribution is recovered, and the result is compared with that of the traditional multi-wavelength iteration algorithm. The relative root mean square value, which represents the phase plane reconstruction accuracy of the algorithm, achieves 10-3 orders of magnitude. Convergence rate is increased by more than two times. In the contrast experiments, multiple sets of light sources with different wavelength number within 635 nm to 785 nm are selected. The results show the method has good fast convergence and high precision phase retrieval ability when the wavelength number is 7 and 8.