Modern Artificial Intelligence (AI) applied to image processing can help you implement face recognition functionalities, detect and recognize objects and actions in images and video, run visual search, and so on.

In this article, we talk about digital image processing and the role of AI. We describe some AI-based image processing tools and techniques that you may use for developing intelligent applications. We also take a look at the most popular neural networks used for different image processing tasks.


What’s image processing?

Image processing methods, techniques, and tools

     Open-source libraries for AI-based image processing

     Machine learning frameworks and platforms for image processing

Using neural networks for image processing


What’s image processing?

Generally speaking, image processing is manipulating an image in order to enhance it or extract information from it. There are two types of image processing methods:

In both cases, the input is an image. For analog image processing, the output is always an image. For digital image processing, however, the output may be an image or some features and characteristics associated with that image.

Today, image processing is widely used in medical visualization, biometrics, self-driving vehicles, gaming, surveillance, and law enforcement. Here are some of the main purposes of image processing:

  • Visualization represents processed data in an understandable way, giving visual form to objects that aren’t visible, for instance.
  • Image sharpening and restoration improves the quality of processed images.
  • Image retrieval helps with image search.
  • Object measurement allows you to measure objects in an image.
  • Pattern recognition helps to distinguish and classify objects in an image, identify their positions, and understand the scene.
Image processing operations

Figure 1. Example of pattern recognition operations

Image credit: Cornell University Computer Vision lectures

Image processing includes eight key phases (Figure 2):

  • Image acquisition is the process of capturing an image with a sensor and converting it into a manageable entity.
  • Image enhancement improves the quality of an input image and extracts hidden details from it.
  • Image restoration removes any possible corruptions (blur, noise, or camera misfocus) from an image in order to get a cleaner version. This process is based mostly on probabilistic and mathematical models.
  • Color image processing includes processing of colored images and different color spaces. Depending on the image type, we can talk about pseudocolor processing (when colors are assigned grayscale values) or RGB processing (for images acquired with a full-color sensor).
  • Image compression and decompression allow for changing the image size and resolution. Compression is responsible for reducing these size and resolution while decompression is used for restoring images to the original.
  • Morphological processing describes the shape and structure of the objects in an image.
  • Image recognition is the process of identifying specific features of particular objects in an image. Image recognition often uses such techniques as object detection, object recognition, and segmentation.
  • Representation and description is the process of visualizing processed data.
Image Processing phases

Figure 2. Main phases of image processing

It’s difficult to accomplish all these tasks manually, especially when it comes to processing massive amounts of data. Here’s where AI and machine learning (ML) algorithms become very helpful.

The use of AI and ML boosts both the speed of data processing and the quality of the final result. For instance, with the help of AI platforms, we can successfully accomplish such complex tasks as object detection, face recognition, and text recognition. But of course, in order to get high-quality results, you need to pick the right tools and methods.

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Image processing methods, techniques, and tools

Most images taken with regular sensors require preprocessing, as they can be misfocused or contain too much noise. Filtering and edge detection are two of the most common methods that can be used for both preprocessing and processing digital images.

There are also other popular techniques for solving image processing tasks. Self-organizing maps, for instance, help to classify images into specific groups. The wavelets technique is widely used for image compression, although it can also be used for denoising.

To make it easier to use these techniques as well as to implement AI-based image processing functionalities in your product, you can use specific libraries and frameworks. In the next section, we take a closer look at some of the most popular open-source libraries for solving different image processing tasks with the help of AI algorithms.

Open-source libraries for AI-based image processing

Computer vision libraries contain common image processing functions and algorithms. Currently, there are several open-source libraries that you can use when developing image processing and computer vision features:

  • OpenCV
  • VXL
  • AForge.NET
  • LTI-Lib


The Open Source Computer Vision Library (OpenCV) is a popular computer vision library that provides hundreds of computer and machine learning algorithms and thousands of functions composing and supporting those algorithms. The library comes with C++, Java, and Python interfaces and supports all popular desktop and mobile operating systems.

OpenCV includes various modules, such as an image processing module, object detection module, and machine learning module. Using this library, you can perform multiple image processing tasks: image acquisition, compression, enhancement, restoration, and data extraction.

OpenCV can use Intel Performance Primitive (IPP) code to speed up image processing. Even though the library isn’t dependent on this code, it will automatically make use of it if your system supports IPP code. The library also supports popular deep learning frameworks including TensorFlow and Caffe.

Read also: Research on Methods for Counting the Number of People in a Video Stream Using OpenCV


The Vision-something-Library (VXL) library is an open-source set of C++ libraries for computer vision. The X is there because for each specific library in the set, the middle letter changes, for instance:

  • VGL – Vision Geometry Library
  • VSL – Vision Streaming Library
  • VIL – Vision Image processing Library

VXL implements a number of popular computer vision technology algorithms and related functionalities.


AForge.NET is an open-source AI and computer vision library developed for the .NET framework. It’s written in C# and consists of multiple libraries that can be used in different fields, from image processing and computer vision to neural networks and fuzzy computations. Additionally, AForge.NET provides help files and a set of sample applications demonstrating how to use this framework.


LTI-Lib is an object-oriented C++ library. It supports Linux and Windows. This library makes it easier to share and maintain code while still providing fast algorithms for real-world applications.

LTI-Lib provides a wide range of features that can be used for solving mathematical problems, a set of classification tools, and multiple image processing and computer vision algorithms.

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Machine learning frameworks and platforms for image processing

If you want to move further than just using simple AI algorithms, you can build custom deep learning models for image processing. To make the development process a bit faster and easier, you can use special platforms and frameworks. Below, we take a closer look at some of the most popular:

  • TensorFlow
  • Caffe
  • MATLAB Image Processing Toolbox
  • Computer Vision by Microsoft
  • Google Cloud Vision
  • Google Colaboratory (Colab)


Google’s TensorFlow is a popular open-source framework with support for machine learning and deep learning. Using TensorFlow, you can create and train custom deep learning models. The framework also includes a set of libraries, including ones that can be used in image processing projects and computer vision applications.


Convolutional Architecture for Fast Feature Embedding (Caffe) is an open-source C++ framework with a Python interface. In the context of image processing, Caffe works best for solving image classification and image segmentation tasks. The framework supports commonly used types of deep learning architectures as well as CPU- and GPU-based accelerated libraries such as Intel MKL and NVIDIA cuDNN.

MATLAB Image Processing Toolbox

MATLAB is an abbreviation for matrix laboratory. It’s the name of both a popular platform for solving scientific and mathematical problems and a programming language. This platform provides an image processing toolbox (IPT), which includes multiple algorithms and workflow applications for image processing, visualization, analysis, and algorithm development.

MATLAB IPT allows you to automate common image processing workflows. This toolbox can be used for noise reduction, image enhancement, image segmentation, 3D image processing, and other tasks. Many of the IPT functions support C/C++ code generation, so they can be used for deploying embedded vision systems and desktop prototyping.

MATLAB IPT isn’t an open-source platform, but it has a free trial version.

Microsoft Computer Vision

Computer Vision is a cloud-based service provided by Microsoft that gives you access to advanced algorithms that can be used for image processing and data extraction. It allows you to perform image processing tasks such as:

  • Analyzing visual features and characteristics of the image
  • Moderating image content
  • Extracting text from images

Google Cloud Vision

Cloud Vision is part of the Google Cloud platform and offers a set of image processing features. It provides an API for integrating such features as image labeling and classification, object localization, and object recognition.

Cloud Vision allows you to use pre-trained machine learning models and create and train custom machine learning models for solving different image processing tasks.

Google Colaboratory (Colab)

Google Colaboratory, otherwise known as Colab, is a free cloud service that can be used not only for improving your coding skills but also for developing deep learning applications from scratch.

Google Colab makes it easier to use popular libraries such as OpenCV, Keras, and TensorFlow when developing an AI-based application. The service is based on Jupyter Notebooks, allowing AI developers to share their knowledge and expertise in a comfortable way. Plus, in contrast to similar services, Colab provides free GPU resources.

In addition to different libraries, frameworks, and platforms, you may also need a large database of images to train and test your model.

There are several open databases containing millions of tagged images that you can use for training your custom machine learning applications and algorithms. ImageNet and Pascal VOC are among the most popular free databases for image processing.

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Using neural networks for image processing

Many of the tools we talked about in the previous section use AI for solving complex image processing tasks. In fact, improvements in AI and machine learning is one of the reasons for the impressive progress in computer vision technology that we can see today.

Most effective machine learning models for image processing use neural networks and deep learning. Deep learning uses neural networks for solving complex tasks similarly to the way the human brain solves them.

Different types of neural networks can be deployed for solving different image processing tasks, from simple binary classification (whether an image does or doesn’t match a specific criteria) to instance segmentation. Choosing the right type and architecture of a neural network plays an essential part in creating an efficient AI-based image processing solution.

Below, we take a look at several popular neural networks and specify the tasks they’re most fit for.

Feedforward Neural Network

A feedforward neural network is one of the simplest types of neural networks. In a feedforward network, data always travels in one direction, from the input nodes to the output nodes, and the connections between the nodes don’t form a cycle. This type of network also may include one or more hidden layers, but the data always moves strictly forward.

Usually, feedforward neural networks are trained with the Backpropagation algorithm. These networks are widely used for image classification and image recognition with AI.

Convolutional Neural Network

Convolutional Neural Networks (ConvNets or CNNs) are a class of deep learning networks that were created specifically for solving image processing tasks. In these networks, the neurons are organized and connected similarly to how neurons are organized and connected in the human brain. In contrast to other neural networks, CNNs require fewer preprocessing operations. Plus, instead of using hand-engineered filters, CNNs can learn the necessary filters and characteristics during training.

CNNs are multilayered neural networks that include input and output layers as well as a number of hidden layers:

  • Convolution layers – Responsible for filtering the input image and extracting specific features such as edges, curves, and colors.
  • Pooling layers – Improve the detection of unusually placed objects.
  • Normalization layers – Improve network performance by normalizing the inputs of the previous layer.
  • Fully connected layers – In these layers, neurons have full connections to all activations in the previous layer (similar to regular neural networks).

All CNN layers are organized in three dimensions (weight, height, and depth) and have two components:

  • Feature extraction
  • Classification

In the first component, the CNN runs multiple convolutions and pooling operations in order to detect features it will then use for image classification.

In the second component, using the extracted features, the network algorithm attempts to predict what the object in the image could be with a calculated probability.

CNNs are widely used for implementing AI in image processing and solving such problems as signal processing, image classification, and image recognition. There are numerous types of CNN architectures such as AlexNet, ZFNet, faster R-CNN, and GoogLeNet/Inception.

The choice of a specific CNN architecture depends on the task at hand. For instance, GoogLeNet shows a higher accuracy for leaf recognition than AlexNet or a basic CNN. At the same time, due to the higher number of layers, GoogLeNet takes longer to run.

Fully Convolutional Network

The concept of a Fully Convolutional Network (FCN) was first offered by a team of researchers from the University of Berkeley. The main difference between a CNN and FCN is that the latter has a convolutional layer instead of a regular fully connected layer. As a result, FCNs are able to manage different input sizes. Also, FCNs use downsampling (striped convolution) and upsampling (transposed convolution) to make convolution operations less computationally expensive.

This type of neural network is the perfect fit for image segmentation tasks when the neural network divides the processed image into multiple pixel groupings which are then labeled and classified. Some of the most popular FCNs used for semantic segmentation are DeepLab, RefineNet, and Dilated Convolutions.

Deconvolutional Neural Network

Deconvolutional Neural Networks (DNNs) are neural networks performing inverse convolutional models where the input data is first unpooled and only then convoluted.

Basically, DNNs use the same tools and methods as convolutional networks but in a different way. This type of neural network is a perfect example of using artificial intelligence for image recognition as well as for analyzing processed images and generating new ones. And, in contrast to regular CNNs, deconvolutional networks can be trained in an unsupervised fashion.

Generative Adversarial Network

Generative Adversarial Networks (GANs) are supposed to deal with one of the biggest challenges neural networks face these days: adversarial images.

Adversarial images are known for causing massive failures in neural networks. For instance, a neural network can be fooled if you add a layer of visual noise called perturbation to the original image. And even though the difference is nearly unnoticeable to the human brain, computer algorithms struggle to classify adversarial images properly (see Figure 3).

Image classification

Figure 3. Example of adversarial image misclassification. Image credit: OpenAI

GANs are double networks that include two nets, a generator and a discriminator, and pit one against the other. The generator is responsible for generating new data and the discriminator is supposed to evaluate that data for authenticity.

Plus, in contrast to other neural networks, GANs can be taught to create new data such as images, music, and prose.


With the help of deep learning algorithms and neural networks, machines can be taught to see and interpret images in the way required for a particular task. Progress in the implementation of AI-based image processing is impressive and opens a wide range of opportunities in fields from medicine and agriculture to retail and law enforcement.

Apriorit developers are curious about AI and machine learning, so we keep track of the latest improvements in AI-powered image processing and use this knowledge when working on our AI projects. We’ll gladly assist you in implementing image processing functionality in your current web application or building a custom AI-based solution from scratch.


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