Whenever we capture images in low-light conditions, the images turn out to be dark. This typically happens when you capture images in the evening or in a dimly lit room. You must have seen this happen many times! The reason this happens is because the pixel values tend to concentrate near 0 when we capture the images under such conditions. When this happens, a lot of details in the image are not clearly visible to the human eye. The human eye likes contrast, and so we need to adjust the contrast to make the image look nice and pleasant. A lot of cameras and photo applications implicitly do this already. We use a process called Histogram Equalization to achieve this.

To give an example, this is what it looks like before and after contrast enhancement:

As we can see here, the input image on the left is really dark. To rectify this, we need to adjust the pixel values so that they are spread across the entire spectrum of values, that is, between 0 and 255.

Following is the code for adjusting the pixel values:

import cv2
import numpy as np

img = cv2.imread('input.jpg', 0)

# equalize the histogram of the input image
histeq = cv2.equalizeHist(img)

cv2.imshow('Input', img)
cv2.imshow('Histogram equalized', histeq)
cv2.waitKey(0)

Histogram equalization is applicable to grayscale images. OpenCV provides a function, equalizeHist, to achieve this effect. As we can see here, the code is pretty straightforward, where we read the image and equalize its histogram to adjust the contrast of the image.

How do we handle color images?

Now that we know how to equalize the histogram of a grayscale image, you might be wondering how to handle color images. The thing about histogram equalization is that it's a nonlinear process. So, we cannot just separate out the three channels in an RGB image, equalize the histogram separately, and combine them later to form the output image. The concept of histogram equalization is only applicable to the intensity values in the image. So, we have to make sure not to modify the color information when we do this.

In order to handle the histogram equalization of color images, we need to convert it to a color space where intensity is separated from the color information. YUV is a good example of such a color space. Once we convert it to YUV, we just need to equalize the Y-channel and combine it with the other two channels to get the output image.

Following is an example of what it looks like:

Here is the code to achieve histogram equalization for color images:

import cv2
import numpy as np

img = cv2.imread('input.jpg')

img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)

# equalize the histogram of the Y channel
img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])

# convert the YUV image back to RGB format
img_output = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)

cv2.imshow('Color input image', img)
cv2.imshow('Histogram equalized', img_output)

cv2.waitKey(0)