目录
论文UIQM（不知道怎么插入pdf，给个名字在这0.0）
原理转自：https://blog.csdn.net/weixin_47169667/article/details/108156530
UISM
（注意此处使用的是RGB颜色空间）
图片被分为K1*K2个区域，log中的分式是每个区域中亮度最大与最小值的比值，即对比度，算出RGB三通道的EME，带入到UISM公式中，其中λc中λR=0.299，λG=0.587，λB=0.114
python简单代码实现
import cv2
import math
import numpy as np
def EME(rbg, L):
m, n = np.shape(rbg) #横向为n列 纵向为m行
number_m = math.floor(m/L)
number_n = math.floor(n/L)
#A1 = np.zeros((L, L)) #创建一个L*L的全0数组用于存放 #用不用没有差别
m1 = 0
E = 0
for i in range(number_m): #将图片按照L*L分成小块 其余的部分舍去
n1 = 0
for t in range(number_n):
A1 = rbg[m1:m1+L, n1:n1+L]
rbg_min = np.amin(np.amin(A1))
rbg_max = np.amax(np.amax(A1))
if rbg_min > 0 :
rbg_ratio = rbg_max/rbg_min
else :
rbg_ratio = rbg_max ###
E = E + np.log(rbg_ratio + 1e-5)
n1 = n1 + L
m1 = m1 + L
E_sum = 2*E/(number_m*number_n)
return E_sum
if __name__ == '__main__':
img = cv2.imread(r'F:\project\wendu\zengqiang_s\test\input\16_img_.jpg') #读取图片
img = cv2.imread(r'F:\project\wendu\zengqiang_s\test\target\16_img_.jpg') #读取图片
r, b, g = cv2.split(img)
EME_r = EME(r, 5) #这里设置的L为5
EME_b = EME(b, 5)
EME_g = EME(g, 5)
uism = 0.299*EME_r + 0.144*EME_b + 0.587*EME_g
print(uism)
原文链接：https://blog.csdn.net/bulubugulu/article/details/115803669
水下图像质量评价指标 UIQM
转自：
水下图像质量评价指标 UIQM（总）_COC0C0的博客-CSDN博客_水下图像质量评价
import cv2
import math
import numpy as np
def uicm(img):
b, r, g = cv2.split(img)
RG = r - g
YB = (r + g)/2 - b
m, n, o = np.shape(img) #img为三维 rbg为二维
K = m*n
alpha_L = 0.1
alpha_R = 0.1 ##参数α 可调
T_alpha_L = math.ceil(alpha_L*K) #向上取整
T_alpha_R = math.floor(alpha_R*K) #向下取整
RG_list = RG.flatten()
RG_list = sorted(RG_list)
sum_RG = 0
for i in range(T_alpha_L+1, K-T_alpha_R ):
sum_RG = sum_RG + RG_list[i]
U_RG = sum_RG/(K - T_alpha_R - T_alpha_L)
squ_RG = 0
for i in range(K):
squ_RG = squ_RG + np.square(RG_list[i] - U_RG)
sigma2_RG = squ_RG/K
YB_list = YB.flatten()
YB_list = sorted(YB_list)
sum_YB = 0
for i in range(T_alpha_L+1, K-T_alpha_R ):
sum_YB = sum_YB + YB_list[i]
U_YB = sum_YB/(K - T_alpha_R - T_alpha_L)
squ_YB = 0
for i in range(K):
squ_YB = squ_YB + np.square(YB_list[i] - U_YB)
sigma2_YB = squ_YB/K
Uicm = -0.0268*np.sqrt(np.square(U_RG) + np.square(U_YB)) + 0.1586*np.sqrt(sigma2_RG + sigma2_YB)
return Uicm
def EME(rbg, L):
m, n = np.shape(rbg) #横向为n列 纵向为m行
number_m = math.floor(m/L)
number_n = math.floor(n/L)
#A1 = np.zeros((L, L))
m1 = 0
E = 0
for i in range(number_m):
n1 = 0
for t in range(number_n):
A1 = rbg[m1:m1+L, n1:n1+L]
rbg_min = np.amin(np.amin(A1))
rbg_max = np.amax(np.amax(A1))
if rbg_min > 0 :
rbg_ratio = rbg_max/rbg_min
else :
rbg_ratio = rbg_max ###
E = E + np.log(rbg_ratio + 1e-5)
n1 = n1 + L
m1 = m1 + L
E_sum = 2*E/(number_m*number_n)
return E_sum
def UICONM(rbg, L): #wrong
m, n, o = np.shape(rbg) #横向为n列 纵向为m行
number_m = math.floor(m/L)
number_n = math.floor(n/L)
A1 = np.zeros((L, L)) #全0矩阵
m1 = 0
logAMEE = 0
for i in range(number_m):
n1 = 0
for t in range(number_n):
A1 = rbg[m1:m1+L, n1:n1+L]
rbg_min = int(np.amin(np.amin(A1)))
rbg_max = int(np.amax(np.amax(A1)))
plip_add = rbg_max+rbg_min-rbg_max*rbg_min/1026
if 1026-rbg_min > 0:
plip_del = 1026*(rbg_max-rbg_min)/(1026-rbg_min)
if plip_del > 0 and plip_add > 0:
local_a = plip_del/plip_add
local_b = math.log(plip_del/plip_add)
phi = local_a * local_b
logAMEE = logAMEE + phi
n1 = n1 + L
m1 = m1 + L
logAMEE = 1026-1026*((1-logAMEE/1026)**(1/(number_n*number_m)))
return logAMEE
if __name__ == '__main__':
img = cv2.imread(r'F:\project\wendu\zengqiang_s\test\target\16_img_.jpg')
r, b, g = cv2.split(img)
Uicm = uicm(img)
EME_r = EME(r, 8)
EME_b = EME(b, 8)
EME_g = EME(g, 8)
Uism = 0.299*EME_r + 0.144*EME_b + 0.587*EME_g
Uiconm = UICONM(img, 8)
uiqm = 0.0282*Uicm + 0.2953*Uism + 3.5753*Uiconm
print("uiqm",uiqm)