引导滤波图像增强python
时间: 2023-09-22 18:10:31 浏览: 112
引导滤波(Guided Filter)是一种非常有效的图像增强方法,它可以保留图像的细节和边缘信息,并能够消除图像中的噪声。下面是使用Python实现引导滤波图像增强的步骤:
1. 导入必要的库:
```python
import cv2
import numpy as np
```
2. 定义引导滤波函数:
```python
def guided_filter(I, p, r, eps):
"""
:param I: guidance image
:param p: filtering input image
:param r: window radius
:param eps: regularization parameter
:return: filtered image q
"""
height, width = I.shape[:2]
N = cv2.boxFilter(np.ones((height, width)), -1, (r, r))
mean_I = cv2.boxFilter(I, cv2.CV_64F, (r, r)) / N
mean_p = cv2.boxFilter(p, cv2.CV_64F, (r, r)) / N
mean_Ip = cv2.boxFilter(I * p, cv2.CV_64F, (r, r)) / N
cov_Ip = mean_Ip - mean_I * mean_p
mean_II = cv2.boxFilter(I * I, cv2.CV_64F, (r, r)) / N
var_I = mean_II - mean_I * mean_I
a = cov_Ip / (var_I + eps)
b = mean_p - a * mean_I
mean_a = cv2.boxFilter(a, cv2.CV_64F, (r, r)) / N
mean_b = cv2.boxFilter(b, cv2.CV_64F, (r, r)) / N
q = mean_a * I + mean_b
return q
```
3. 读取图像并进行增强:
```python
img = cv2.imread("image.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转为灰度图像
# 高斯平滑,消除噪声
img_smoothed = cv2.GaussianBlur(img, (7, 7), 0)
# 计算梯度图像
I_x = cv2.Sobel(img_smoothed, cv2.CV_64F, 1, 0, ksize=3)
I_y = cv2.Sobel(img_smoothed, cv2.CV_64F, 0, 1, ksize=3)
I_gradient = np.sqrt(I_x**2 + I_y**2)
# 进行引导滤波,保留细节和边缘信息
r = 5 # 窗口大小
eps = 0.1 # 正则化参数
q = guided_filter(I_gradient, img, r, eps)
# 归一化
q = cv2.normalize(q, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
```
4. 显示增强后的图像:
```python
cv2.imshow("Enhanced Image", q)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
上述代码中,通过引导滤波的方式,对灰度图像进行了增强,保留了图像的细节和边缘信息。可以根据需要调整窗口大小和正则化参数来达到更好的增强效果。
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