这篇教程ACE算法利用局部标准差,可以让图像中对比度较弱的部分的增强效果更加明显写得很实用,希望能帮到您。
在同一图像中,需要增强对比度不强的部分。方差表示的是图像的像素值的均匀性,可以认为方差越大的局部区域,其像素值越不均匀,对比度越强;反之,方差越小的局部区域,其像素值越均匀,对比度越弱。 因此,ACE算法利用局部标准差,可以让图像中对比度较弱的部分的增强效果更加明显。
import cv2
import numpy as np
import math
def stretchImage(data, s=0.005, bins = 2000):
ht = np.histogram(data, bins);
d = np.cumsum(ht[0])/float(data.size)
lmin = 0; lmax=bins-1
while lmin<bins:
if d[lmin]>=s:
break
lmin+=1
while lmax>=0:
if d[lmax]<=1-s:
break
lmax-=1
return np.clip((data-ht[1][lmin])/(ht[1][lmax]-ht[1][lmin]), 0,1)
g_para = {}
def getPara(radius = 5):
global g_para
m = g_para.get(radius, None)
if m is not None:
return m
size = radius*2+1
m = np.zeros((size, size))
for h in range(-radius, radius+1):
for w in range(-radius, radius+1):
if h==0 and w==0:
continue
m[radius+h, radius+w] = 1.0/math.sqrt(h**2+w**2)
m /= m.sum()
g_para[radius] = m
return m
def zmIce(I, ratio=4, radius=300):
para = getPara(radius)
height,width = I.shape
zh,zw = [0]*radius + [x for x in range(height)] + [height-1]*radius, [0]*radius + [x for x in range(width)] + [width -1]*radius
Z = I[np.ix_(zh, zw)]
res = np.zeros(I.shape)
for h in range(radius*2+1):
for w in range(radius*2+1):
if para[h][w] == 0:
continue
res += (para[h][w] * np.clip((I-Z[h:h+height, w:w+width])*ratio, -1, 1))
return res
def zmIceFast(I, ratio, radius):
height, width = I.shape[:2]
if min(height, width) <=2:
return np.zeros(I.shape)+0.5
Rs = cv2.resize(I, ((width+1)//2, (height+1)//2))
Rf = zmIceFast(Rs, ratio, radius)
Rf = cv2.resize(Rf, (width, height))
Rs = cv2.resize(Rs, (width, height))
return Rf+zmIce(I,ratio, radius)-zmIce(Rs,ratio,radius)
def zmIceColor(I, ratio=4, radius=3):
res = np.zeros(I.shape)
for k in range(3):
res[:,:,k] = stretchImage(zmIceFast(I[:,:,k], ratio, radius))
return res
if __name__ == '__main__':
m = zmIceColor(cv2.imread('4.jpg')/255.0)*255
cv2.imwrite('zmIce.jpg', m)
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