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

Question

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.

Answer 1

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: