A guide to AI based image processing: Phases, techniques and tools

ai based image processing | iTMunch

The method of manipulating images to either extract relevant information from it or enhance its quality is called image processing. Image processing is extremely useful and its demand is continuously growing. The market for image recognition is projected to grow to US$38.92 billion in 2021 at a CAGR of 19.5% [1]. Thanks to the recent advancements in technology, artificial intelligence can be used in image processing. In this blog, we’ll talk about digital image processing, the phases of AI based image processing and a few tools and techniques you can use for the same. 

Historically speaking, image processing that utilizes machine learning appeared first in the 1960s. It was an attempt to simulate the human vision system as well as automate the image analysis process. As the image processing technology developed and enhanced, radical solutions for specific tasks began to emerge, and soon AI in image processing also began gaining popularity. 

Now, let us begin by understanding what is image processing.

What is image processing?

Generally speaking, image processing is the method of performing operations on images, in order to obtain enhanced images or to extract information from it. There are two ways of image processing:

  • Analog image processing: Traditional image processing that’s used for processing physical photographs, hard copies of images and printouts 
  • Digital image processing: Used for manipulating digital images by using computer algorithms like machine learning and artificial intelligence 

In both cases, analog and digital, the input is the same (an image). However, for analog image processing, the output will always be an image, but for digital image processing, output might be an enhanced image of information associated with that image. This information can be data on characteristics, features, masks or bounding boxes.

The use of image processing

Image processing is used in various fields and industries today including self-driving vehicles, medical visualization, biometrics, surveillance and law enforcement. Following are a few purposes of image processing:

  • Image sharpening and image restoration: Enhancing the quality of processed images
  • Visualization: Representing processed data in an easily understandable and sophisticated manner by giving visual forms to objects which are not visible 
  • Image retrieval: Aids in image search 
  • Pattern recognition: Distinguishing and classifying objects in images, identifying their positions and understanding the scene 
  • Object measurement: Measuring objects in images

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Phases in image processing

Following are the 8 key phases or stages in digital image processing: 

1. Image Acquisition 

This is the phase in which an image is captured using a sensor (say, a camera) and converted into a manageable entity (such as a digital image file). Scraping is a popular image acquisition method.

2. Image Enhancement 

This phase improves image quality to extract hidden details and information for further processing.

3. Image Restoration 

This stage is also focused at improving the quality of the image. It is done by removing potential corruptions to get a better and clearer image. Image restoration is based on mathematical and probabilistic models and is used to eliminate blur, missing pixels, noise, watermarks, camera misfocus and other corruptions.

4. Color Image Processing

This step revolves around the processing of different color spaces and colored images. Depending on the type of image, you can use RGB processing (for an image captured with a full-color sensor) and Pseudocolor processing (in which colors are assigned grayscale values).

5. Image Compression & Decompression 

This phase allows you to change the size and resolution of the image. Decompression is used to restore an image to its original resolution and size and compression is used to reduce the resolution and size. This is used during the image augmentation process as well. 

6. Morphological Processing 

It describes the structures and shapes of objects in an image. Techniques of morphological processing can be used when you are creating datasets for training artificial intelligence models. Particularly, morphological analysis/processing is applied at the stage of annotation, when you describe what exactly you want to detect and recognize in your AI model.

7. Image Recognition 

This is the process of identifying certain features of particular objects in images. This stage, combined with AI, uses techniques like object recognition, object detection and segmentation. This is where the power of artificial intelligence comes into limelight. 

8. Representation & Description

The last phase revolves around the visualization and description of processed data. 

AI solutions and systems are designed to work as effectively and efficiently as possible. While image processing has proven to be helpful in various industries, processing images in bulk manually is not easy. This is where AI image processing comes in. The use of machine learning and artificial intelligence in image processing has boosted the data processing speed and also enhanced the overall quality of the result produced. However, in order to get quality results, picking the right tools and techniques is crucial.

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AI based Image processing techniques

When it comes to AI image processing of digital images, filtering and edge detection are the 2 most common methods. Let’s have a look at the two methods:

  • Filtering: It is one of the most common techniques for AI image enhancements and modifications. Basically, through filtering, you can emphasize or remove some features from an image with the help of filters. Reduction of image noise is also possible through filtering. Most used filtering techniques are median filtering, linear filtering and Wiener filtering
  • Edge detection: Another technique of AI image processing is edge detection that uses image segmentation as well as data extraction. This AI image processing technique helps in finding edges of objects by detecting discontinuities in brightness. Sobel edge detection, Canny edge detection and Roberts edge detection are a few common ways of edge detection used across the world

Though AI based image processing is groundbreaking, it is a complicated process. So, to make things convenient, there are open-source libraries as well as frameworks that can be used to execute image processing. 

Open-source libraries for AI image processing

1. Open Source Computer Vision Library (OpenCV)

OpenCV is a trusted computer vision library providing hundreds of computer, ML and AI algorithms. It also has thousands of functions that compose and support these algorithms. OpenCV also comes with Java, C++ and Python interfaces and is compatible with all popular mobile and desktop operating systems. It includes multiple modules, like object detection module, AI based image processing module, machine learning module, etc. Using this open-source library, you can acquire, enhance, compress, extract and restore data from images.

2. VGG Image Annotator (VIA)

VGG Image Annotator is a web-based application that’s designed for object annotation. VIA can be installed directly through a web browser. It is used for annotating detected objects not just in images but in audio and video records as well. VIA is user-friendly, does not require installation or additional setup and can be used easily with any modern browser.

3. Visualization Library 

Visualization Library is basically a C++ middleware for 2 Dimension (2D) and 3 Dimension (3D) apps based on the OpenGL (Open Graphics Library). It is a comprehensive toolkit that allows you to build high-performance portable apps for Windows, Mac OS X and Linux systems. As most of the Visualization Library classes are equipped with intuitive one-to-one mapping with functions of the OpenGL library, Visualization Library middleware is easy and comfortable to work with. 

AI based image processing frameworks & platforms


MATLAB stands for ‘matrix laboratory’. It is a platform known for both, solving scientific as well as mathematical problems and a programming language. MATLAB platform offers an Image Processing Toolbox (IPT) which includes several workflow and algorithms applications for visualizing, processing and analyzing images and for developing algorithms. MATLAB IPT enables the automation of common image processing workflows. It can also be used for image enhancement, noise reduction, 3D image processing, image segmentation and other tasks. Though MATLAB IPT is not an open-source platform, it has a free trial.

2. Google Cloud Vision 

Cloud Vision, a part of the Google Cloud platform, offers a suite of AI based image processing features. It offers an API for integrating features such as object localization, object recognition and image labeling and classification. Cloud Vision also allows you to utilize pre-trained ML models and then create and train custom ML models for accomplishing different image processing tasks.

3. Google Colaboratory (Colab) 

Google Colaboratory, commonly known as Colab, is a free and efficient cloud service that can be used for enhancing your coding skills as well as developing deep learning apps from the ground up. Colab from Google makes it easier to use libraries like OpenCV, TensorFlow and Keras when developing an AI application. 


With the help of deep learning, artificial intelligence and neural networks, machines can be taught to interpret images in a way that’s required for a particular task. Image processing is a growing artificial intelligence trend. Advancements in the execution of AI based image processing have been impressive in the past years. It has opened up a broad range of opportunities in industries like medicine, agriculture, retail and law enforcement.

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[1] Comserve (2021) “Image Recognition Market Global Forecast 2021 by Global Trends, Leading Key Players, Future Growth, Revenue Analysis, Demand Forecast” [Online] Available form: https://www.comserveonline.com/news-releases/image-recognition-market-global-forecast-2021-by-global-trends-leading-key-players-future-growth-revenue-analysis-demand-forecast/10047882 [Accessed March 2021]

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