# 图像清晰度评价指标（Python）

### 评估方法实现

github：图像清晰度评估算法包（有示例）

#### 1 Brenner 梯度函数

```            ```
def brenner(img):
'''
:param img:narray 二维灰度图像
:return: float 图像约清晰越大
'''
shape = np.shape(img)
out = 0
for x in range(0, shape[0]-2):
for y in range(0, shape[1]):
out+=(int(img[x+2,y])-int(img[x,y]))**2
return out

```
```

#### 2 Laplacian梯度函数

```            ```
def Laplacian(img):
'''
:param img:narray 二维灰度图像
:return: float 图像约清晰越大
'''
return cv2.Laplacian(img,cv2.CV_64F).var()

```
```

#### 3 SMD（灰度方差）

```            ```
def SMD(img):
'''
:param img:narray 二维灰度图像
:return: float 图像约清晰越大
'''
shape = np.shape(img)
out = 0
for x in range(0, shape[0]-1):
for y in range(1, shape[1]):
out+=math.fabs(int(img[x,y])-int(img[x,y-1]))
out+=math.fabs(int(img[x,y]-int(img[x+1,y])))
return out

```
```

#### 4 SMD2（灰度方差乘积）

```            ```
def SMD2(img):
'''
:param img:narray 二维灰度图像
:return: float 图像约清晰越大
'''
shape = np.shape(img)
out = 0
for x in range(0, shape[0]-1):
for y in range(0, shape[1]-1):
out+=math.fabs(int(img[x,y])-int(img[x+1,y]))*math.fabs(int(img[x,y]-int(img[x,y+1])))
return out

```
```

#### 5 方差函数

```            ```
def variance(img):
'''
:param img:narray 二维灰度图像
:return: float 图像约清晰越大
'''
out = 0
u = np.mean(img)
shape = np.shape(img)
for x in range(0,shape[0]):
for y in range(0,shape[1]):
out+=(img[x,y]-u)**2
return out

```
```

#### 6 能量梯度函数

```            ```
def energy(img):
'''
:param img:narray 二维灰度图像
:return: float 图像约清晰越大
'''
shape = np.shape(img)
out = 0
for x in range(0, shape[0]-1):
for y in range(0, shape[1]-1):
out+=((int(img[x+1,y])-int(img[x,y]))**2)+((int(img[x,y+1]-int(img[x,y])))**2)
return out

```
```

#### 7 Vollath函数

```            ```
def Vollath(img):
'''
:param img:narray 二维灰度图像
:return: float 图像约清晰越大
'''
shape = np.shape(img)
u = np.mean(img)
out = -shape[0]*shape[1]*(u**2)
for x in range(0, shape[0]-1):
for y in range(0, shape[1]):
out+=int(img[x,y])*int(img[x+1,y])
return out

```
```

#### 8 熵函数

```            ```
def entropy(img):
'''
:param img:narray 二维灰度图像
:return: float 图像约清晰越大
'''
out = 0
count = np.shape(img)[0]*np.shape(img)[1]
p = np.bincount(np.array(img).flatten())
for i in range(0, len(p)):
if p[i]!=0:
out-=p[i]*math.log(p[i]/count)/count
return out

```
```

[1] 图像清晰度的评价指标

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