Dear All,

The following is an example given in opencv regarding applying Contrast Limited Adaptive Histogram Equalization (CLAHE)

import numpy as np

import cv2

img = cv2.imread('tsukuba_l.png',0)

clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))

cl1 = clahe.apply(img)

Here the parameter clipLimit =2.0

In Skimage, CLAHE  is perfored using exposure.equalize_adapthist

For instance, in this example, http://scikit-image.org/docs/dev/auto_examples/plot_equalize.html   

img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)

My question is that how to setup the clip_limit value in skimage for a corresponding case in opencv

For instance, in an example implemented using opencv, clipLimit is setup as 2.0; if I want to convert this implementation using skimage

which value should I assign to clip_limit?

According to the document  looks like clip_limit between 0 and 1. 

clip_limit : float, optional

Clipping limit, normalized between 0 and 1 (higher values give more contrast).

while opencv does not have this limitation for clipLimit

Thanks,

Yuanyuan

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