Dear Yuanyuan, There is no strict correspondence between these two clip limits. If you would like to have something like OpenCV implementation of CLAHE, consider trying https://github.com/anntzer/clahe. Also, feel free to join the discussion in https://github.com/scikit-i mage/scikit-image/issues/2219. There you might find a bit more details. Regards, Egor 2016-12-27 2:22 GMT+03:00 wine lover <winecoding@gmail.com>:

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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