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In Development Last updated: June 8, 2023
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We perceive the world around us mostly visually. Humans are able to identify objects, perceive distance, and even guess texture just by seeing images.

Yet, despite how common and easy seeing seems to be, it remains somewhat elusive when trying to program computers to perform it. However, there are algorithms that have been developed over the years to help computers perform this seemingly daunting task of image processing. T

These algorithms have been implemented in code and distributed as software libraries that we will cover in this article.

What is Image Processing?

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Image processing is a part of computer vision. It involves the tasks performed on images in preparation for using them in machine learning and, broadly, artificial intelligence.

It is useful for detecting and identifying objects in images. This would be applicable to building self-driving cars and robots and classifying images to identify things like hateful content.

Applications of Image Processing

Image processing is used in a number of fields. Some of its most common and useful applications include:

  • Medical imaging and diagnosis where AI can diagnose patients from scan
  • Security when implementing biometric security systems and building surveillance systems.
  • Robotics when building robots capable of seeing their environment. These include self-driving cars.
  • Augmented reality, for example, filters. Image processing is used to identify objects and apply filters to them.
  • Traffic analysis to read number plates. This can be used in law enforcement to identify drivers who do not stop at red traffic lights or in speed traps.

There are various algorithms used in image processing. These include morphological dilation and erosion, Gaussian, Fourier Transform, Edge detection, and wavelet image processing.

In the absence of software libraries, one would have to implement these algorithms from scratch. Thankfully, there are prewritten libraries that implement the required functionality. This is a list of some of the most common.


OpenCV is one of the most popular image-processing libraries. It is used for implementing computer vision algorithms and performing machine learning and image processing.

In addition, it is available for free and is open-source. Furthermore, OpenCV is versatile as it can be used with a variety of programming languages. These include Python, C++, and Java.

The library implements many of the functions and algorithms you will need for image processing. These include edge detection, feature extraction, transformations, rotations, resizing, and enhancing.


Scikit-image offers a user-friendly and efficient way to perform image processing. It enables image input/output, transformation, filtering, segmentation, feature extraction, image restoration, and geometric transformations.

The library integrates with other scientific computing libraries and enables users to easily manipulate and enhance images for various applications, from basic operations like resizing to advanced tasks like object recognition or image restoration.


SimpleITK is a simplified version of ITK. ITK (Insight Toolkit) is a cross-platform and open-source library for image analysis. SimpleITK supports reading and writing images from more than 20 image file formats.

In addition to Python, it works with other programming languages such as R, Java, Ruy, Lua, C++, and C#. It provides a wide range of functions that can be used for working with images.

SimpleITK is also powerful as it supports working with 2D, 3D, 4D, and 5D images. It also supports parallel processing to speed up operations.


SciPy is a cousin to the popular numerical analysis library, NumPy. SciPy is used for scientific computing, whereas NumPy is used for numerical computation. Because SciPy uses NumPy under the hood, it is very fast and efficient.

It also supports working with multidimensional images. The library implements functions to perform filtering, morphology, object measurements, and B-spline interpolation.


Pillow is a continuation of the Python Imaging Library(PIL). PIL was a library for working with images in Python. However, it only worked with Python2 and was discontinued in 2011.

Pillow is a fork of the project. It supports reading images from different file formats to its own internal representation. Pillow’s internal representation manipulates images efficiently and provides fairly-powerful image processing functions. It also supports fast access to data and performing operations such as rotations and resizing.


pgmagick is an open-source Python library written by Hideo Huttori. It acts as a wrapper for GrphicsMagick, which is a collection of tools and libraries used to read, write and manipulate images.

The library supports more than 88 different image formats and can perform several functions such as get the size of images, sharpen or blur images, detect edges, swirl them, solarise, compare images, and write them to disk.

pgmagick can be used to create gifs, draw text and convert images from different file formats.

Final Words

While this list is not exhaustive, the libraries listed above are some of the most popular libraries used in image processing. They have been used by many people and organizations to implement systems capable of object detection, segmentation, and analysis.

This has been incredibly useful in the fields of artificial intelligence and robotics. As an alternative to libraries, you can use APIs for image analysis.

  • Anesu Kafesu
    Full stack web developer and technical writer. Currently learning AI.
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