Image histogram matching is a technique used to match the histogram of one image to the histogram of another image. This process can be helpful in various image processing applications, such as image enhancement and color correction. In this blog post, we will explore how to perform histogram matching using the popular Python imaging library, Pillow.

Getting Started with Pillow

Pillow is a powerful library for handling and manipulating images in Python. You can install it using pip:

pip install pillow

Once you have Pillow installed, you can import it in your script or Python console:

from PIL import Image

Loading Images

To begin with, we need to load the source image and the reference image that we want to match the histogram of. We can use the Image.open() function of Pillow to load images from file:

source_image = Image.open('source_image.png')
reference_image = Image.open('reference_image.png')

Make sure that the source and reference images are of the same size, as histogram matching requires the images to have the same dimensions.

##Histogram Matching

Now that we have loaded the source and reference images, we can proceed with histogram matching. Pillow provides a convenient method called histogram_match() that performs the histogram matching operation. This method takes the reference image as an argument and returns a new image with the histogram matched to the reference image:

matched_image = source_image.histogram_match(reference_image)

Saving the Result

Finally, we can save the matched image to a file using the save() method of the Image class:

matched_image.save('matched_image.png')

Conclusion

In this blog post, we learned how to perform histogram matching using the Pillow library in Python. Histogram matching is a valuable technique for image processing, allowing us to match the histogram of one image to that of another. By following these steps, you can easily implement histogram matching in your own Python projects.