Image processing is a method to perform operations on an image to extract information from it or enhance it. Digital image processing has a broad range of applications such as image restoration, medical imaging, remote sensing, image segmentation, etc. Every process requires a different technique.

In this article, we will be covering the top 6 image processing techniques for machine learning.

1. Image Restoration
2. Linear Filtering
3. Independent Component Analysis
4. Pixelation
5. Template Matching
6. Image Generation Technique (GAN)

See also: Best Image Processing Tools Used in Machine Learning

1. Image restoration

Source

An image deteriorates for many reasons, for example, an old image of your grandparents which was taken with the old tech camera could become hazy or may lose its original form.

This could happen if the image goes under some physical stress or if it’s in digital form it could deteriorate by motion blur or additive noise.

So how are you going to restore it? Maybe it wasn’t possible 50 years back but now – it is.

Researchers came up with a Degradation model** that can undo the deterioration effects on the input image. The degradation model works as a convolution with a linear shift-invariant.**

So we take an Image before the degradation which is called “True Image” and an Image after degradation which is called “Observed Image” with the degradation filter which estimates the “True Image”.

An example of image restoration using image inpainting with OpenCV

Image impainting also known as **“Compensation of paint loss ”. **This technique is often used to remove unwanted objects from an image to restore damaged parts of a deteriorated image.

import cv2

img = cv2.imread('damaged_image.png')
mask = cv2.imread('mask.png', 0)
dst = cv2.inpaint(img, mask, 3, cv2.INPAINT_NS)

cv2.imwrite('restored.png', dst)

In the above code, we have two types of images

1. Damaged
2. Masked

A masked image has the same spatial dimensions of the noise which exists in the noisy image.

So if we input the image below with the above code:

With the mask:

Then we will get the following image:

The biggest problem with OpenCV’s image inpainting is that we need to manually input a mask for the specific image we want to fix. So how can we automate this process?

_The answer is _GAN (General Adversarial Network). This paper proposes that, by using a GAN network, image inpainting can be done using neighborhood loss function and gradient loss with a better quality restored image.

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