Digital image processing is a common buzzword these days. It is used almost in every field, ranging from medical science to entertainment. To those who are not acquainted with what digital image processing is, it is a technique that uses computer systems to manipulate and enhance images to get desired results. Its definition itself describes why image processing has become an integral part of multiple industries and applications. This blog will take through the basics of digital image processing techniques and the uses of image processing. Read the blog till the end to find out the link to an image processing ebook that is free to download and answers all your questions related to the technology. So, without further ado, let’s begin! 

What are Image Processing Techniques? 

Image processing involves multiple stages. It includes importing or fetching the image, analyzing and manipulating it, and finally providing the output. While image processing is of two types, analog and digital, we’ll be covering digital image processing techniques in this blog. 

The technology of digital image processing is based on computer algorithms. Researchers and developers make use of different algorithms to find solutions to distinct tasks. A task can be anything from image detection to image enhancement or reconstruction. 

Image processing techniques deal with the formation, acquisition, and processing of images. Following are some major digital image processing techniques. 

  • Image Editing: A commonly used term, image editing refers to altering or enhancing digital images with the help of graphic designing software. 
  • Image Restoration: At times, an image may get degraded due to different reasons. Image restoration is a technique to recover the original image from a corrupted version. The main goal behind image restoration is to get ‘real’ information and not change or alter it to please the observer. 
  • Anisotropic Diffusion: This technique is also called Perona-Malik Diffusion. Anisotropic diffusion helps to reduce or remove noise from an image without altering its critical elements. 
  • Independent Component Analysis: Independent Component Analysis is an image processing technique that computationally separates a multivariate signal into additive subcomponents. 
  • Pixelation: This technique in image processing refers to transforming printed pictures or images into digital ones, such as GIFs (Graphics Interchange Format). 
  • Neural Networks: Neural networks are components of Artificial Intelligence that simulate the functioning and working of a human brain to solve multiple tasks. 
  • Linear Filtering: Linear filtering is one of the crucial image processing techniques. This technique produces output signals that are subject to linearity constraints. 
  • Principal Components Analysis: This image processing technique is used for feature extraction. 
  • Hidden Markov Models: With Hidden Markov Models, users can analyze images in two dimensions. 
  • Partial Differential Equations: Partial Differential Equations are used for de-noising pictures or images. 
  • Self-Organizing Maps: This technique classifies pictures or images into different classes. 
  • Wavelets: These are wave-like oscillations that are used for compressing images. 

Uses of Digital Image Processing 

Below are some common uses of digital image processing. 

  • Encoding and Transmission 
  • Video Processing 
  • Medical Imaging 
  • Image Restoration and Sharpening 
  • Microscopic Imaging 
  • Pattern Recognition 
  • Computer Vision 
  • Color Processing 

Download this Free Image Processing eBook 

If you want to dig deeper into image processing, then this eBook has you covered. You will learn everything, right from image processing basics to color space conversion. Click here to download your copy.