以下是将给定的改进型角谱迭代算法的 MATLAB 代码转换为 Python 代码： ```python import numpy as np import cv2 import time # 参数初始化 lambda_ = 632.8e-6 # 波长 d = 20 # 衍射距离 mm N = 256 # 像素 PIESIZE = 8e-3 # 像素大小 L = N * PIESIZE # 长宽 k = 2 * np.pi / lambda_ # 波矢 step = 500 # 迭代次数 loss = np.ones(step) # MSE psn = np.zeros(step) # psnr b = 1.1 # 修正量 a = 0.8 gk = np.zeros((N, N)) minloss = 1 # 读入 A0 = cv2.imread('3d=20.tif', cv2.IMREAD_GRAYSCALE) A0 = A0 / 255.0 A = np.ones((N, N)) phasek = 2 * np.pi * np.random.rand(N, N) # 给出随机的初始相位 phasek1 = phasek Ei = A * np.exp(1j * phasek) # 初始的角谱面 # 频域初始化 x, y = Ei.shape fX = np.concatenate((np.arange(0, np.floor(x / 2) + 1), np.arange(np.ceil(x / 2) - 1, 0, -1))) / L fY = np.concatenate((np.arange(0, np.floor(y / 2) + 1), np.arange(np.ceil(y / 2) - 1, 0, -1))) / L fx, fy = np.meshgrid(fX, fY) # 角谱传播函数 f = fx ** 2 + fy ** 2 H = np.exp(1j * k * d * np.sqrt(1 - (lambda_ ** 2) * f)) HB = 1 / H start_time = time.time() # 开始迭代 for n in range(step): EOO = np.fft.ifft2(np.fft.fft2(Ei) * H) AOO = np.abs(EOO) ** 2 AOO = AOO / np.max(AOO) EO = A0 * np.exp(1j * np.angle(EOO)) # angle(EOO)是新计算得到的新相位 （像面） Eii = np.fft.ifft2(np.fft.fft2(EO) * HB) faik = np.angle(Eii) # 计算得到的新相位 （物面） faik = faik / np.max(faik) beitak = (phasek - phasek1) if n > 1: gk1 = gk gk = faik - phasek rk = np.sum(gk * gk1) / np.sum(gk1 ** 2) phasek1 = phasek phasek = faik + beitak * rk phasek = phasek / np.max(phasek) else: gk = faik - phasek phasek = faik Ei = np.exp(1j * phasek) loss[n] = np.mean((A0 - AOO) ** 2) psn[n] = 10 * np.log10(1 / loss[n]) cv2.imshow('faik', faik) cv2.waitKey(1) if loss[n] < minloss: pass minloss = np.min(loss) end_time = time.time() print(f"Time taken: {end_time - start_time} seconds") # 显示结果 cv2.imshow('原图', A0) cv2.imshow('恢复', AOO) faik = (faik - np.min(faik)) / (np.max(faik) - np.min(faik)) faik = (faik * 255).astype(np.uint8) cv2.imshow('恢复的相位', faik) cv2.waitKey(0) cv2.destroyAllWindows() # 保存数据 # np.savetxt('MSE.txt', loss) # np.savetxt('PSNR.txt', psn) ```