图像增强python
时间: 2023-08-25 21:15:51 浏览: 104
图像增强是通过一系列的算法和技术来改善图像的视觉质量。在Python中,有多种方法可以实现图像增强。
一种方法是使用反色变换。反色变换是将图像的每个像素值与最大灰度值进行差值计算,以增强暗背景下的图像细节。以下是一个使用反色变换的Python代码示例:
```
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
def image_inverse(input):
value_max = np.max(input)
output = value_max - input
return output
gray_img = np.asarray(Image.open('X.jpg').convert('L'))
inv_img = image_inverse(gray_img)
plt.subplot(121)
plt.title('original')
plt.imshow(gray_img, cmap='gray', vmin=0, vmax=255)
plt.subplot(122)
plt.title('inverse')
plt.imshow(inv_img, cmap='gray', vmin=0, vmax=255)
plt.show()
```
另一种常用的图像增强方法是雾霾去除。雾霾去除是通过计算图像的暗通道来估计全球大气光,然后根据透射率调整图像的亮度和对比度。以下是一个使用OpenCV库实现雾霾去除的Python代码示例:
```
import cv2
import numpy as np
def hazeRemoval(img, w=0.7, t0=0.1):
darkChannel = img.min(axis=2)
A = darkChannel.max()
darkChannel = darkChannel.astype(np.double)
t = 1 - w * (darkChannel / A)
t[t < t0 = t0
J = img.copy()
J[:, :, 0 = (img[:, :, 0 - (1 - t) * A) / t
J[:, :, 1 = (img[:, :, 1 - (1 - t) * A) / t
J[:, :, 2 = (img[:, :, 2 - (1 - t) * A) / t
return J
hazeImage = cv2.imread("image.jpg")
result = hazeRemoval(hazeImage.copy())
cv2.imshow("HazeRemoval", np.hstack([hazeImage, result]))
cv2.waitKey(0)
```
除了上述方法之外,还可以使用其他图像处理技术来实现图像增强,比如直方图均衡化、对比度增强等。具体使用哪种方法取决于需要实现的具体效果和应用场景。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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