When photographing a sheet of paper (e.g. with phone camera), I get the following result (left image) (jpg download here). The desired result (processed manually with an image editing software) is on the right:

I would like to process the original image with openCV to get a better brightness/contrast automatically (so that the background is more white).

Assumption: the image has an A4 portrait format (we don't need to perspective-warp it in this topic here), and the sheet of paper is white with possibly text/images in black or colors.

What I've tried so far:

1. Various adaptive thresholding methods such as Gaussian, OTSU (see OpenCV doc Image Thresholding). It usually works well with OTSU:

   ret, gray = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU + cv2.THRESH_BINARY)
   

   but it only works for grayscale images and not directly for color images. Moreover, the output is binary (white or black), which I don't want: I prefer to keep a color non-binary image as output

2. Histogram equalization

   • applied on Y (after RGB => YUV transform)
   • or applied on V (after RGB => HSV transform),

   as suggested by this answer (Histogram equalization not working on color image - OpenCV) or this one (OpenCV Python equalizeHist colored image):

   img3 = cv2.imread(f)
   img_transf = cv2.cvtColor(img3, cv2.COLOR_BGR2YUV)
   img_transf[:,:,0] = cv2.equalizeHist(img_transf[:,:,0])
   img4 = cv2.cvtColor(img_transf, cv2.COLOR_YUV2BGR)
   cv2.imwrite('test.jpg', img4)
   

   or with HSV:

   img_transf = cv2.cvtColor(img3, cv2.COLOR_BGR2HSV)
   img_transf[:,:,2] = cv2.equalizeHist(img_transf[:,:,2])
   img4 = cv2.cvtColor(img_transf, cv2.COLOR_HSV2BGR)
   

   Unfortunately, the result is quite bad since it creates awful micro contrasts locally (?):

   

   I also tried YCbCr instead, and it was similar.

3. I also tried CLAHE (Contrast Limited Adaptive Histogram Equalization) with various tileGridSize from 1 to 1000:

   img3 = cv2.imread(f)
   img_transf = cv2.cvtColor(img3, cv2.COLOR_BGR2HSV)
   clahe = cv2.createCLAHE(tileGridSize=(100,100))
   img_transf[:,:,2] = clahe.apply(img_transf[:,:,2])
   img4 = cv2.cvtColor(img_transf, cv2.COLOR_HSV2BGR)
   cv2.imwrite('test.jpg', img4)
   

   but the result was equally awful too.

4. Doing this CLAHE method with LAB color space, as suggested in the question How to apply CLAHE on RGB color images:

   import cv2, numpy as np
   bgr = cv2.imread('_example.jpg')
   lab = cv2.cvtColor(bgr, cv2.COLOR_BGR2LAB)
   lab_planes = cv2.split(lab)
   clahe = cv2.createCLAHE(clipLimit=2.0,tileGridSize=(100,100))
   lab_planes[0] = clahe.apply(lab_planes[0])
   lab = cv2.merge(lab_planes)
   bgr = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
   cv2.imwrite('_example111.jpg', bgr)
   

   gave bad result too. Output image:

   

5. Do an adaptive thresholding or histogram equalization separately on each channel (R, G, B) is not an option since it would mess with the color balance, as explained here.

6. "Contrast strechting" method from scikit-image's tutorial on Histogram Equalization:

   the image is rescaled to include all intensities that fall within the 2nd and 98th percentiles

   is a little bit better, but still far from the desired result (see image on top of this question).

TL;DR: how to get an automatic brightness/contrast optimization of a color photo of a sheet of paper with OpenCV/Python? What kind of thresholding/histogram equalization/other technique could be used?

This method should work well for your application. First you find a threshold value that separates the distribution modes well in the intensity histogram then rescale the intensity using that value.

from skimage.filters import threshold_yen
from skimage.exposure import rescale_intensity
from skimage.io import imread, imsave

img = imread('mY7ep.jpg')

yen_threshold = threshold_yen(img)
bright = rescale_intensity(img, (0, yen_threshold), (0, 255))

imsave('out.jpg', bright)

I'm here using Yen's method, can learn more about this method on this page.