Image Processing & Machine Learning Redefining the Future

Sara Al-Mahrouq

We are currently dealing with an influx of massive amounts of data in the form of images and videos due to the increasing prevalence of cameras, webcams, optical sensors, etc. Even though visualizing any type of information is always simpler, processing such a large amount of imagery is more challenging than it sounds. The heart of machine learning—currently a popular topic for research and experimentation—makes the system learn details that it can recognize in subsequent encounters. As a result, data processing becomes very efficient.

Combining machine learning advancements with the rising problem of image processing has resulted in a highly efficient streamlined system where Machine Learning models speed up the classification, segmentation, and recognition of images, videos, footage, etc.

In this article, we will talk about the sophisticated Machine Learning techniques currently being applied to image and video processing:

1. Image Restoration

An image can deteriorate for a variety of reasons. For instance, an old photograph of your grandparents shot using an old tech camera may become hazy or lose its original shape. The image might have experienced physical stress or, if the image is in digital form, suffered from motion blur or additive noise. So, how do you intend to repair it? Perhaps fifty years ago, it wasn't possible, but today it is.

Researchers developed a degradation model that can reverse the effects of deterioration on the input image. The degradation model works as a convolution with a linear shift-invariant.

Therefore, using a degradation filter that estimates the True Image. We take two images: one before degradation, known as the True Image, and one after degradation, known as the Observed Image.

2. Linear filtering

When using linear filtering, the neighboring input pixels are combined linearly to determine the value of the output pixel, a process known as convolution is used. Convolution adds each image component to its nearby neighbors while using the kernel's weighting.

3. Template matching

Template matching is a technique for finding and locating templates within a large image. Think of it as a very simplistic approach to object detection.

Template matching slides a template image over a larger image, similar to folding, and looks for matching parts.