使用 OpenCV 恢复图像以增强细节

Question

我正在尝试恢复和增强几张照片的图像细节。我尝试通过使用简单的内核提高清晰度来cv2.filter2D()显示细节。

我尝试过边缘检测内核

[-1 -1 -1]
[-1  9 -1]
[-1 -1 -1]

和锐化内核

[ 0 -1  0]
[-1  5 -1]
[ 0 -1  0]

但结果看起来有颗粒感且不自然。为了消除噪音，我尝试了诸如cv2.medianBlur()和 之类的模糊技术cv2.GaussianBlur()，但结果并不那么好。图像背景模糊或较暗，导致特征难以区分。有没有更好的方法来显示更多细节，尤其是在背景中？对 Python 或 C++ 开放

输入图像

当前结果

import numpy as np
import cv2

img = cv2.imread('people.jpg')
grayscale = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# edge_kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
sharpen_kernel = np.array([[0,-1,0], [-1,5,-1], [0,-1,0]])
img = cv2.filter2D(grayscale, -1, sharpen_kernel)

# Smooth out image
# blur = cv2.medianBlur(img, 3)
blur = cv2.GaussianBlur(img, (3,3), 0)

cv2.imshow('img',img)
cv2.imwrite('img.png',img)
cv2.imshow('blur',blur)
cv2.waitKey(0)

Answer 1

直方图均衡在这里可以通过扩展强度范围来提高灰度图像的对比度。这是输入图像直方图的可视化

from matplotlib import pyplot as plt
import cv2

# Load in image as grayscale
image = cv2.imread('1.jpg', 0)

plt.hist(image.ravel(), 256, [0,256])

像素聚集在中间范围强度周围。为了增加图像的对比度，直方图均衡化在整个范围内拉伸强度值以获得更宽且更均匀的分布。您可以使用内置函数来做到这一点，cv2.equalizeHist()

equalize = cv2.equalizeHist(image)
plt.hist(equalize.ravel(), 256, [0,256])

强度范围现在均匀分布。直方图均衡考虑了图像的全局对比度，并且当图像的直方图局限于特定区域时效果很好。这是结果

In some cases where there are intensity variations across a large region, CLAHE (Contrast Limited Adaptive Histogram Equalization) may be better. CLAHE is implemented in OpenCV as cv2.createCLAHE()

clahe = cv2.createCLAHE().apply(image)
plt.hist(clahe.ravel(), 256, [0,256])

This variation divides pixels into small blocks before performing adaptive histogram equalization.

Here's a visualization between the two methods