医学图像处理案例（十七）——基于小波变换和自适应脉冲耦合神经网络的图像融合

pip install PyWavelets

import pywt
import cv2
import numpy as np
import math
from scipy import signal


def pcnn(img, link=5, beta=0.1, iteration=200):
    """
    PCNN generate fire maps image
    :param img:source image
    :param link:pcnn link parm
    :param beta:pcnn link coefficient
    :param iteration:pcnn iteration number
    :return:fire maps image
    """
    m, n = np.shape(img)[0], np.shape(img)[1]
    alpha_L = 1
    alpha_Theta = 0.2
    vL = 1.0
    vTheta = 20
    center_x = round(link / 2)
    center_y = round(link / 2)
    W = np.zeros((link, link))
    for i in range(link):
        for j in range(link):
            if i == center_x and j == center_y:
                W[i, j] = 0
            else:
                W[i, j] = 1. / math.sqrt(pow(i - center_x, 2) + pow(j - center_y, 2))

    imgf = img.astype(np.float)
    F = abs(imgf)
    L = np.zeros((m, n))
    Y = np.zeros((m, n))
    Theta = np.zeros((m, n))
    img_pcnn = np.zeros((m, n))

    for i in range(iteration):
        K = signal.convolve2d(Y, W, mode='same')
        L = math.exp(-alpha_L) * L + vL * K
        Theta = math.exp(-alpha_Theta) * Theta + vTheta * Y
        U = np.multiply(F, 1 + np.multiply(beta, L))
        Y = U > Theta
        Yf = Y.astype(np.float)
        img_pcnn = img_pcnn + Yf
    return img_pcnn


# This function does the coefficient fusing according to the fusion method
def fuseCoeff(cooef1, cooef2, method):
    if (method == 'mean'):
        cooef = (cooef1 + cooef2) / 2
    elif (method == 'min'):
        cooef = np.minimum(cooef1, cooef2)
    elif (method == 'max'):
        cooef = np.maximum(cooef1, cooef2)
    elif (method == 'pcnn'):
        pcnn1 = pcnn(cooef1, 5, 0.1, 200)
        pcnn2 = pcnn(cooef2, 5, 0.1, 200)
        pcnn_cooef = np.maximum(pcnn1, pcnn2)
        cooef1[pcnn1 != pcnn_cooef] = 0
        cooef2[pcnn2 != pcnn_cooef] = 0
        cooef = cooef1 + cooef2
    elif (method == 'auto_pcnn'):
        W = [[-1, -4, -1], [-4, 20, -4], [-1, -4, -1]]
        K1 = signal.convolve2d(cooef1, W, mode='same')
        K2 = signal.convolve2d(cooef2, W, mode='same')
        beta1 = np.sum(np.multiply(K1, K1))
        beta2 = np.sum(np.multiply(K2, K2))
        pcnn1 = pcnn(cooef1, 5, beta1, 200)
        pcnn2 = pcnn(cooef2, 5, beta2, 200)
        pcnn_cooef = np.maximum(pcnn1, pcnn2)
        cooef1[pcnn1 != pcnn_cooef] = 0
        cooef2[pcnn2 != pcnn_cooef] = 0
        cooef = cooef1 + cooef2
    return cooef


# Params
FUSION_METHOD = 'mean'  # Can be 'min' || 'max || anything you choose according theory
FUSION_METHOD1 = 'auto_pcnn'
# Read the two image
I1 = cv2.imread('IR1.png', 0)
I2 = cv2.imread('VIS1.png', 0)
# First: Do wavelet transform on each image
wavelet = 'db2'
cooef1 = pywt.wavedec2(I1[:, :], wavelet, level=1)
cooef2 = pywt.wavedec2(I2[:, :], wavelet, level=1)
# Second: for each level in both image do the fusion according to the desire option
fusedCooef = []
for i in range(len(cooef1)):
    # The first values in each decomposition is the apprximation values of the top level
    if (i == 0):
        fusedCooef.append(fuseCoeff(cooef1[0], cooef2[0], FUSION_METHOD))
    else:
        # For the rest of the levels we have tupels with 3 coeeficents
        c1 = fuseCoeff(cooef1[i][0], cooef2[i][0], FUSION_METHOD1)
        c2 = fuseCoeff(cooef1[i][1], cooef2[i][1], FUSION_METHOD1)
        c3 = fuseCoeff(cooef1[i][2], cooef2[i][2], FUSION_METHOD1)
        fusedCooef.append((c1, c2, c3))
# Third: After we fused the cooefficent we nned to transfor back to get the image
fusedImage = pywt.waverec2(fusedCooef, wavelet)
# Forth: normmalize values to be in uint8
fusedImage1 = np.multiply(np.divide(fusedImage - np.min(fusedImage), (np.max(fusedImage) - np.min(fusedImage))), 255)
fusedImage1 = fusedImage1.astype(np.uint8)
# Fith: Show image
cv2.imwrite("win.bmp", fusedImage1)