Histogram Equalization is a basic image processing technique to boost contrasts, the histogram equalization is often used in the pre-processing phase to make video more plausible.

However, a straight way of histogram equalization can incur strong contouring effects (and consequently an encoder would consume more bits for retention of these superfluous edges). Moreover, by boosting of contrasts we might make camera noise visible.

The histogram equalization technique maps actual histogram of pixel intensities in an input grayscale/single-channel image to uniform:

 

 

 

One of the problems – boosting the contrast can boost the noise in the image and make contouring:

To reduce contouring a special method CLAHE of adaptive histogram equalization is commonly applied:

 

Fortunately OpenCV contains CLAHE histogram equalization method (cv2.createCLAHE), the following python script demonstrates how to apply CLAHE:

img = cv2.imread(jpgfile)     # assumed JPEG is in BGR format

          gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)   # convert BGR to gray

          equalized = cv2.equalizeHist(gray)   # apply straightforward histogram equalization

clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))   # init CLAHE method

adaptequalized = clahe.apply(gray)     # apply CLAHE method

cv2.imshow(“Original”, gray)

cv2.imshow(‘HistEqualized’, equalized)

cv2.imshow(‘AdaptHistEqualized’, adaptequalized)

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