图像融合质量评价方法的python代码实现——MS-SSIM_msssim

1 前言

在评估融合图像质量时，由于作者使用的是python代码进行融合，但有些评价指标只有matlab代码就十分不方便，所以将其重写为python代码，之前已经实现了$Q^{AB/F}$的python代码，详见图像融合质量评价方法的python代码实现——Qabf。

2 MS-SSIM介绍

MS-SSIM由Z. Wang，E.P. Simoncelli等人提出，是一种多尺度结构相似性方法，在结合观看条件的变化方面，它比以前的单尺度方法具有更大的灵活性。从Multiscale structural similarity for image quality assessment中的实验结果表明，在适当的参数设置下，多尺度方法的性能优于最佳的单尺度SSIM模型以及最新的图像质量指标。公式如下：$$MSSSIM(Z,K)={\left[l_M(Z,K)\right]}^{{\alpha }_M}\prod _{i=1}^M{\left[s_i(Z,K)\right]}^{{\beta }_i}{\left[z_i(Z,K)\right]}^{{\gamma }_i}$$

2 MS-SSIM的代码

2.1 matlab代码

matlab的代码可以看图像融合质量评价方法MSSIM、MS-SSIM、FS、Qmi、Qabf与VIFF（三）

2.2 python代码

python代码如下，有两部分SSIM.py与MSSSIM.py，首先是SSIM.py:

#ssim.py
import numpy as np
import scipy.signal


def ssim_index_new(img1,img2,K,win):

    M,N = img1.shape

    img1 = img1.astype(np.float32)
    img2 = img2.astype(np.float32)

    C1 = (K[0]*255)**2
    C2 = (K[1]*255) ** 2
    win = win/np.sum(win)

    mu1 = scipy.signal.convolve2d(img1,win,mode='valid')
    mu2 = scipy.signal.convolve2d(img2,win,mode='valid')
    mu1_sq = np.multiply(mu1,mu1)
    mu2_sq = np.multiply(mu2,mu2)
    mu1_mu2 = np.multiply(mu1,mu2)
    sigma1_sq = scipy.signal.convolve2d(np.multiply(img1,img1),win,mode='valid') - mu1_sq
    sigma2_sq = scipy.signal.convolve2d(np.multiply(img2, img2), win, mode='valid') - mu2_sq
    img12 = np.multiply(img1, img2)
    sigma12 = scipy.signal.convolve2d(np.multiply(img1, img2), win, mode='valid') - mu1_mu2

    if(C1 > 0 and C2>0):
        ssim1 =2*sigma12 + C2
        ssim_map = np.divide(np.multiply((2*mu1_mu2 + C1),(2*sigma12 + C2)),np.multiply((mu1_sq+mu2_sq+C1),(sigma1_sq+sigma2_sq+C2)))
        cs_map = np.divide((2*sigma12 + C2),(sigma1_sq + sigma2_sq + C2))
    else:
        numerator1 = 2*mu1_mu2 + C1
        numerator2 = 2*sigma12 + C2
        denominator1 = mu1_sq + mu2_sq +C1
        denominator2 = sigma1_sq + sigma2_sq +C2

        ssim_map = np.ones(mu1.shape)
        index = np.multiply(denominator1,denominator2)
        #如果index是真，就赋值，是假就原值
        n,m = mu1.shape
        for i in range(n):
            for j in range(m):
                if(index[i][j] > 0):
                    ssim_map[i][j] = numerator1[i][j]*numerator2[i][j]/denominator1[i][j]*denominator2[i][j]
                else:
                    ssim_map[i][j] = ssim_map[i][j]
        for i in range(n):
            for j in range(m):
                if((denominator1[i][j] != 0)and(denominator2[i][j] == 0)):
                    ssim_map[i][j] = numerator1[i][j]/denominator1[i][j]
                else:
                    ssim_map[i][j] = ssim_map[i][j]

        cs_map = np.ones(mu1.shape)
        for i in range(n):
            for j in range(m):
                if(denominator2[i][j] > 0):
                    cs_map[i][j] = numerator2[i][j]/denominator2[i][j]
                else:
                    cs_map[i][j] = cs_map[i][j]


    mssim = np.mean(ssim_map)
    mcs = np.mean(cs_map)

    return  mssim,mcs

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然后是MSSSIM.py：

#msssim.py
import numpy as np
import cv2
from index.SSIM import ssim_index_new

def msssim(img1,img2):

    K = [0.01,0.03]
    win  = np.multiply(cv2.getGaussianKernel(11, 1.5), (cv2.getGaussianKernel(11, 1.5)).T)  # H.shape == (r, c)
    level = 5
    weight = [0.0448,0.2856,0.3001,0.2363,0.1333]
    method = 'product'

    M,N = img1.shape
    H,W = win.shape

    downsample_filter = np.ones((2,2))/4
    img1 = img1.astype(np.float32)
    img2 = img2.astype(np.float32)

    mssim_array = []
    mcs_array = []

    for i in range(0,level):
        mssim,mcs = ssim_index_new(img1,img2,K,win)
        mssim_array.append(mssim)
        mcs_array.append(mcs)
        filtered_im1 = cv2.filter2D(img1,-1,downsample_filter,anchor = (0,0),borderType=cv2.BORDER_REFLECT)
        filtered_im2 = cv2.filter2D(img2,-1,downsample_filter,anchor = (0,0),borderType=cv2.BORDER_REFLECT)
        img1 = filtered_im1[::2,::2]
        img2 = filtered_im2[::2,::2]

    print(np.power(mcs_array[:level-1],weight[:level-1]))
    print(mssim_array[level-1]**weight[level-1])
    overall_mssim = np.prod(np.power(mcs_array[:level-1],weight[:level-1]))*(mssim_array[level-1]**weight[level-1])

    return overall_mssim

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2.3 效果对比

将MRI图像与融合后的图像进行评估，如下：

matlab代码运行结果如下：

python代码运行结果如下：

可以看到两种代码运行结果是一致的。

3 总结

这个代码还有一个问题在于不能处理24位的图像，不过目前正在进行FMI的matlab代码改python代码，如果完成会继续分享…