How to do a localized Contrast Enhancement In a scanned Image Using OpenCV Python

I was working on a project I wanted to perform a localized contrast enhancement / adaptive contrast enhancement on a couple of images. I have tried thresholding but it is affecting the text of the image. I am attaching the images below

Source: ImageHere

Result: ImageHere

Global contrast and other features are not working. Please do not suggest CLAHE It is giving very weird results. Please help me thank you.

Here is one way to do that in Python/OpenCV using division normalization and some sharpening.

• Read the input
• Convert to grayscale
• Blur the image
• Divide the grayscale image by the blurred image
• Apply sharpening (as desired)
• Save the results

Input:

import cv2
import numpy as np
import skimage.filters as filters

# read the image
img = cv2.imread('math_questions.jpg')

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

# blur
smooth = cv2.GaussianBlur(gray, (95,95), 0)

# divide gray by morphology image
division = cv2.divide(gray, smooth, scale=255)

# sharpen using unsharp masking
result = filters.unsharp_mask(division, radius=1.5, amount=1.5, multichannel=False, preserve_range=False)
result = (255*result).clip(0,255).astype(np.uint8)

# save results
cv2.imwrite('math_question_division.jpg',division)
cv2.imwrite('math_question_division_sharpen.jpg',result)

# show results
cv2.imshow('smooth', smooth)  
cv2.imshow('division', division)  
cv2.imshow('result', result)  
cv2.waitKey(0)
cv2.destroyAllWindows()

Division image:

Sharpened result: