The general histogram equalization formula for an 8-bit image is: h(v) = ((cdf(v) - cdf_min) / (M * N - cdf_min) * 255) where

cdf(v) is the cumulative distribution function. For each pixel v, cdf(v) equals to the number of pixels with values lower or equal to v cdf_min is the minimum non-zero value of the cumulative distribution function M × N are the sizes of the image

I want to calculate cdf(v) which I can do using iterating over all the pixels but I am wondering if there's a way to do it in an efficient way using numpy so that the whole operation is way faster.

CodePudding user response：

The cdf is the prefix sum of the pdf. You can compute that prefix sum yourself in Python (just 255 iterations), or rely on a ready-made function. https://www.geeksforgeeks.org/prefix-sum-array-python-using-accumulate-function/#:~:text=A prefix sum is a,using accumulate(iterable) method.

CodePudding user response：

Just use cdf = np.cumsum(pdf) on your histogram/PDF. That calculates the cumulative sum. It's faster than writing it yourself in python, especially if your histogram is already a numpy array.

All of that has already been asked and answered before on Stack Overflow, so this question ought to be closed as a duplicate of How to get the cumulative distribution function with NumPy?