直方图均衡化



Python+OpenCV学习(5)---直方图

对比度受限的自适应直方图均衡化(CLAHE)

Histograms - 2: Histogram Equalization



Histograms Equalization

cv2.equalizeHist()



CLAHE (Contrast Limited Adaptive Histogram Equalization)


So to solve this problem, adaptive histogram equalization is used. In this, image is divided into small blocks called “tiles” (tileSize is 8x8 by default in OpenCV). Then each of these blocks are histogram equalized as usual. So in a small area, histogram would confine to a small region (unless there is noise). If noise is there, it will be amplified. To avoid this, contrast limiting is applied. If any histogram bin is above the specified contrast limit (by default 40 in OpenCV), those pixels are clipped and distributed uniformly to other bins before applying histogram equalization. After equalization, to remove artifacts in tile borders, bilinear interpolation is applied.


import numpy as np
import cv2
img = cv2.imread('tsukuba_l.png',0)
# create a CLAHE object (Arguments are optional).
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
cl1 = clahe.apply(img)
cv2.imwrite('clahe_2.jpg',cl1)





opencv doc

OpenCV2-Python-Tutorials

https://docs.opencv.org/2.4.13.4/

Python+OpenCV学习(2)---图像的合并与拆分

OpenCV图像增强算法实现(直方图均衡化、拉普拉斯、Log、Gamma)

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