8 Different Image Processing Techniques You Can Use

Image processing is a subfield of technology that involves the manipulation of images to enhance their quality, extract valuable information, or make them suitable for further analysis.

We can broadly categorize image processing into two, namely digital image processing and analog image processing. However, in this article, we’ll focus more on digital image processing, which is the use of a digital computer to process digital image through a sequence of processes, such as an algorithm.

This article will also introduce some of the basic concepts behind image processing and various image processing techniques that are widely used in industries, such as healthcare, photography, security, business, and even social media. Let’s dive in!

In this article:

• What Is Image Processing (And Why Does It Matter?)
• Image Editing
• Image Segmentation
• Image Compression
• Image Upscaling
• Image Registration
• Image Restoration
• Object Recognition and Detection
• Image Synthesis

What Is Image Processing (And Why Does It Matter?)

Image processing is a loose term that has different meanings, depending on the context it’s used.

In computer vision, image processing refers to the techniques used to analyze, manipulate, and interpret visual data to enable tasks such as object detection, pattern recognition, and image classification.

In photography and image editing, it involves enhancing, altering, or manipulating images to improve visual quality or achieve creative effects, such as adjusting contrast, color correction, sharpening, or applying filters.

In both cases, image processing involves manipulating or applying transformation to images to achieve a desired result. The techniques used in image processing help transform these raw images into clearer, more useful formats.

Image Editing

Image editing is transforming and manipulating images to enhance their visual appeal or to prepare them for specific uses. Its usually applied to raster images, which are types of image in which the image’s data is stored as a grid of picture elements, or pixels. Examples of image editing processes include cropping, color adjustments, resizing, background removal, removing or adding objects, and applying filters.

Cloudinary provides a Media Editor with an interactive user interface providing a set of common image editing features which you can use on your website or application. You can also access several image editing features using any of Cloudinary’s programmable SDKs.

The applications of image editing cut across many fields, including photography and digital art, social media content creation (think about memes), marketing and advertisements, and many others.

Example of edited image:

Image Segmentation

Image segmentation is a computer vision technique that involves dividing an image into different parts or segments for object detection and other machine learning tasks. It processes visualize data at the pixel level, using various techniques to annotate individual pixels as belonging to a specific class or instance. The goal is to divide the image into meaningful parts or objects. The segmentation mask produced as the output represents the boundaries and shapes of different classes or regions within the image.

There are two major approaches to achieving image segmentation: classical computer vision approaches and AI-based (deep learning) approaches.

• Classical methods use predefined rules or heuristics like pixel color, intensity, or texture to group similar pixels for segmentation. These techniques are straightforward and efficient, but struggle with complex scenes and variations in image quality.
• AI-based methods employ neural networks to learn patterns from large datasets, enabling highly accurate and adaptive segmentation even in complex, overlapping, or noisy images. They are powerful, but require extensive data and computational resources.

The major goal of segmentation is to simplify and/or change an image’s representation such that it is more meaningful and easier to evaluate.

Image Compression

Image compression is reducing an image’s byte size by removing redundant or irrelevant data in order to store or transmit the data in an efficient form. There are two major types of image compression, namely lossy and lossless compression. Lossy compression involves permanently removing unnecessary metadata and artifacts in an image, while lossless compression reduces file size by removing unnecessary metadata or non-essential elements that don’t impact the quality of the image.

Website optimization commonly involves using image compression to achieve faster load times, in file storage and sharing, and soon. There are several tools available for image compression, ranging from browser-based tools to programmable solutions like Cloudinary.

Cloudinary’s intelligent quality and encoding algorithm analyzes an image to find the best quality compression level and optimal encoding settings based on the image content and the viewing browser, in order to produce an image with good visual quality while minimizing the file size. You can use the q_auto transformation parameter in your image’s delivery URL to achieve optimal balance between file size and visual quality.

Let’s look at an example:

• Original image
• Compressed image
  Applying the q_auto:best parameter significantly reduced the image’s size without a noticeable difference between the original and compressed images.

  Image Upscaling

  Image upscaling, also known as image enlargement, is an image processing technique that involves increasing the resolution and size of an image without reducing its quality. Image upscaling is usually applied to images that appear blurry or pixelated to increase their resolution and quality. The methods of upscaling images can be categorized broadly into two, traditional and AI-based methods.

  The traditional method involves interpolation, which uses mathematical algorithms to predict the missing pixel values in an image. While this method can produce great results, it often leads to pixelation and loss of detail, resulting in a less visually appealing image. AI-based methods on the other hand, use machine learning algorithms and deep learning models to analyze the existing pixels and generate high-resolution images.

  Cloudinary’s AI-driven upscaling technology uses advanced machine learning models to add fine details and quality while upscaling small images.

  To upscale an image using Cloudinary, you can use the e_upscale parameter. For example, to upscale the following 200 x 303 pixel image to 800 x 1212 pixels:

  

  All we need to do is add the e_upscale parameter to the image’s delivery URL. Here’s what the upscaled image looks like:

  

  Image Registration

  Image registration is the process of aligning or comparing two or more images of the same scene taken at different times or from different viewpoints, in order to transform the image data into a single coordinate system. For example, aligning satellite images from different dates for comparison.

  Image registration is used in a variety of industries, including medical imaging (for combining MRI and CT images of patients), military and surveillance (satellite imaging for change detection over time), and computer vision (multi-view object analysis).

  

  A registered image of two MRI scans of the brain. Source.

  Image Restoration

  Image restoration is the process of recovering the original pixel information from blurred, corrupted, or noisy images. Factors such as poor lighting, aging photos, low-quality cameras, and various environmental conditions contribute to the degradation of image quality.

  There are several techniques used for image restoration. each targeting specific types of distortion or degradation. Examples include:

  • Deconvolution: This restores sharpness by reversing motion or out-of-focus blur using methods like Wiener filtering and blind deconvolution, which estimate and reverse the effects of the blur.
  • Super-Resolution: This method enhances the resolution of an image by reconstructing high-frequency details from a low-resolution input, often using convolutional neural networks (CNNs).
  • Fourier Transform: Uses frequency-domain techniques to correct periodic noise or other repetitive artifacts in an image.

  Cloudinary offers Generative Restore, an advanced technology that uses Generative AI to restore old and damaged images. This power tool can be used in enhancing user generated contents, old and damaged images, or poorly compressed images.

  You can use the e_gen_restore transformation parameter: https://res.cloudinary.com/demo/image/upload/e_gen_restore/docs/old-photo.jpg.
  

  Object Recognition and Detection

  Object recognition and object detection are two important processes in computer vision. Although the two concepts are sometimes used interchangeably, there’s a marked difference between what each process aims to accomplish.

  Object recognition is identifying and classifying objects in an image or video. Object recognition gives a high-level description of an image or video without providing additional context or information about the image.

  Object detection, on the other hand, is finding and locating objects of interest in an image or video, typically by adding a bounding box with a label around each of the detected objects.

  The image below explains the difference between the two processes:

  

  Many fields and industries use object detection and recognition for various purposes such as security, surveillance, self-driving cars, medical imaging, robotics, and augmented reality. For example, object detection can help spot faces or license plates on security cameras, detect people and cars for autonomous vehicles, or find tumors in medical scans. Object recognition can identify faces and fingerprints in biometric systems, read handwritten text in OCR software, or recognize objects and scenes in augmented reality applications.

  The Cloudinary AI Content Analysis add-on uses AI-based object detection and content-aware algorithms to provide functionalities, such as AI-based image captioning (similar to image recognition) and automatic image tagging (for object and content-aware detection).

  Image Synthesis

  Image synthesis is the process of generating new images from scratch or from incomplete data, often using AI techniques like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). These techniques can create realistic images of people, objects, or scenes that never existed.

  Image synthesis has diverse applications across various fields, including biology and medicine , entertainment (for creating realistic CGI for movies, video games, and virtual reality experiences), art (for creative designing by combining or altering existing images).

  Image Processing At Scale

  Processing a thousand images or more is an arduous task that requires a significant amount of computational resources and technical management. Handling large volumes of images efficiently demands not only robust infrastructure but also scalable solutions capable of automating tasks such as resizing, optimizing, and transforming images in real-time. This is where Cloudinary comes in as a powerful cloud-based solution.

  Cloudinary simplifies image processing at scale by offering a comprehensive platform that automates much of the workflow. With Cloudinary, you can upload images once and then apply various transformations like cropping, resizing, or adjusting colors on the fly using simple URLs. In addition, you can also leverage Cloudinary Add-ons platform to enjoy powerful image processing capabilities provided by third-party image solution providers.

  Transform and optimize your images and videos effortlessly with Cloudinary’s cloud-based solutions. Sign up for free today!