灰度图的直⽅图均衡化（HistogramEqualization）原理与
Python实现天外飞星
原理
直⽅图均衡化是⼀种通过使⽤图像直⽅图，调整对⽐度的图像处理⽅法；通过对图像的强度（intensity）进⾏某种⾮线性变换，使得变换后的图像直⽅图为近似均匀分布，从⽽，达到提⾼图像对⽐度和增强图⽚的⽬的。普通的直⽅图均衡化采⽤如下形式的⾮线性变换:
设f 为原始灰度图像，g 为直⽅图均衡化的灰度图像，则g 和f 的每个像素的映射关系如下：
齐秦个人资料简介
其中，L 为灰度级，通常为 256，表明了图像像素的强度的范围为 0 ~ L-1;
p n 等于图像f 中强度为 n 的像素数占总像素数的⽐例，即原始灰度图直⽅图的概率密度函数；
f i,j 表⽰在图像f 中，第 i ⾏，第 j 列的像素强度；
g i,j 表⽰在图像g 中，第 i ⾏，第 j 列的像素强度.
Python 实现心存善念
#!/usr/bin/env python
# -*- coding: utf8 -*-
"""
炸葱油饼的做法# Author: klchang
# Date: 2018.10
# Description:
histogram equalization of a gray image.
"""
from__future__import print_function
import numpy as np
import matplotlib.pyplot as plt
def histequ(gray, nlevels=256):
# Compute histogram
histogram = np.bincount(gray.flatten(), minlength=nlevels)
print ("histogram: ", histogram)
# Mapping function
uniform_hist = (nlevels - 1) * (np.cumsum(histogram)/(gray.size * 1.0))
uniform_hist = uniform_hist.astype('uint8')
print ("uniform hist: ", uniform_hist)
风清月朗
# Set the intensity of the pixel in the raw gray to its corresponding new intensity
唤醒height, width = gray.shape
uniform_gray = np.zeros(gray.shape, dtype='uint8') # Note the type of elements
for i in range(height):
for j in range(width):
uniform_gray[i,j] = uniform_hist[gray[i,j]]
return uniform_gray
if__name__ == '__main__':
fname = "320px-Unequalized_Hawkes_Bay_NZ.png"# Gray image
# Note, matplotlib natively only read png images.
gray = plt.imread(fname, format=np.uint8)
if gray is None:
print ("Image {} does not exist!".format(fname))
exit(-1)
# Histogram equalization
uniform_gray = histequ(gray)
# Display the result
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.t_title("Raw Image")
ax1.imshow(gray, 'gray')
ax1.t_xticks([]), ax1.t_yticks([])
ax2.t_title("Histogram Equalized Image")
ax2.imshow(uniform_gray, 'gray')
ax2.t_xticks([]), ax2.t_yticks([])
fig.tight_layout()
plt.show()拼音本写拼音
原始图⽚ 320px-Unequalized_Hawkes_Bay_NZ.png
结果显⽰
参考资料
[1]. Histogram_equalization - Wikipedia. en.wikipedia/wiki/Histogram_equalization
[2]. Histogram Equalization. www.math.uci.edu/icamp/cours/math77c/demos/hist_eq.pdf孙灏
