传统耦合神经网络（pcnn）算法的实现（python）：

参数的设定没有具体参考，这是一篇文献中的解释：

# coding:utf-8 #
from PIL import Image
from pylab import *
from scipy import signal as sg
from PCNN.noise import sp_noiseimport numpy as npclass Pcnn_class():def PCNN(self, img_arr, iteration_num,Af = 0.60, Al = 1.0, Atop = 0.80,Vf = 0.2, Vl = 1, Vtop = 3000.0,Beta = 0.1):# def PCNN(self, img_arr, iteration_num,#          Af = 0.06931, Al = 1.47, Atop = 0.80,#          Vf = 20, Vl = 1.9, Vtop = 3000.0,#          Beta = 0.16):# 获得矩阵的维度x_len, y_len = img_arr.shapeprint(" shape: ",x_len,y_len)# 定义神经元矩阵，建立n=0时的F、L矩阵（都为0矩阵）W = np.array([[0.7070, 1, 0.7070], [1, 0, 1], [0.7070, 1, 0.7070]])M = np.array([[0.7070, 1, 0.7070], [1, 0, 1], [0.7070, 1, 0.7070]])Y = np.zeros_like(img_arr, dtype="float64")F = np.zeros_like(img_arr, dtype="float64")L = np.zeros_like(img_arr, dtype="float64")top = np.zeros_like(img_arr, dtype="float64")# 定义点火过程for i in range(0, iteration_num):K = sg.convolve2d(Y, M, boundary='symm', mode="same")print ("第 %s 次迭代，K矩阵：" % i, K)F = exp(-Af) * F + Vf * K + img_arrL = exp(-Al) * L + Vl * K# print "第 %s 次迭代，L矩阵：" % i, "\n", LU = F * (1 + Beta * L)top = exp(-Atop) * top + Vtop * Yfor x_axis in range(0, x_len):for y_axis in range(0, y_len):if (U[x_axis, y_axis] > top[x_axis, y_axis]):Y[x_axis, y_axis] = 1.0else:Y[x_axis, y_axis] = 0.0print ("第 %s 次迭代完成。\n" % i)print(len(Y[Y == 0]))print(len(Y[Y == 1]))return Y###################################################################### 获得图像矩阵
if __name__ == "__main__":img = Image.open(r'C:\Users\dell\Desktop\bs\test-14-MR.jpg').convert('L')img = np.array(img)noise_img = sp_noise(img, 0.05)p=Pcnn_class()img_out = p.PCNN(img_arr=img, iteration_num=10)print ("done!")fig,(ax1,ax2,ax3)=plt.subplots(1,3,figsize=(10,6)) #建立1行2列的图figax1.imshow(noise_img,cmap='gray') #显示原始的图ax1.set_axis_off() #不显示坐标轴ax1.set_title('PIC1_L')ax2.imshow(img_out,cmap='gray') #显示原始的图ax2.set_axis_off() #不显示坐标轴ax2.set_title('PIC2_L')ax3.imshow(img, cmap='gray')  # 显示原始的图ax3.set_axis_off()  # 不显示坐标轴ax3.set_title('origil')plt.show()