A Brief Guide To AI-based Image Processing
Artificial intelligence (AI) has had numerous breakthroughs over the past few years. Computer vision is one such breakthrough and is one of the most recent trends in the tech industry.
Computer vision is a field of AI that enables computers to acquire, analyze, and derive data from digital objects (images, videos, text). It involves numerous tasks, some of which require tools to perform. Data labeling, for instance, one of the sub-domains of computer vision, typically involves an image annotation tool platform. Image processing is yet another task and is an essential component as it lays the foundation of computer vision.
A Brief Overview of Image Processing
Image processing is the act of using a computer to convert a real-world scene into a digital image and performing various operations to derive valuable data from it. These operations may include enhancing the image’s quality, analyzing its colors, and compressing its size. Think of it as the technique that computers use to see images the same way human eyes do. It’s an essential component of computer vision mainly because it turns images into insightful data.
What is AI Image Processing?
AI image processing follows the same concept as image processing. It aims to extract valuable data from digital images by performing various operations, which you’ll come to know in the succeeding sections. The main difference between the two is that AI image processing utilizes AI based technologies to accelerate the task, allowing you to process countless pictures more quickly. Since AI projects often require large amounts of data, such technology is an incredibly valuable asset.
But much like working with different digital technologies, one must have prior knowledge of image processing to utilize the technology properly. That’s why you’re here—to understand how AI image processing works.
Read on as this article tackles the basics of AI image processing, starting with its components.
AI Image Processing: Components
The main objective of AI image processing is to derive large amounts of data from a dataset consisting of photos. To achieve this, various tasks are performed, as listed below:
- Recognition: Detect images on the image
- Visualization: Find objects and details in an image that aren’t visible to the naked eye
- Pattern recognition: Distinguish certain patterns within the image
- Enhancement: Minimize an image’s noise to improve quality and maximize recognizability
- Retrieval: Browse large databases and locate images and other digital files that match the characteristics of the processed image
These techniques are what constitute an AI image processing project. When performing image processing, an AI tool utilizes one or more of these techniques, especially during the key stages.
AI Image Processing: Fundamental Stages
Just like any other technique, AI image processing consists of numerous steps or stages. Each stage is essential and will determine the success of the AI technology’s implementation.
AI image processing is comprised of ten fundamental stages, as explained below:
- The Acquisition is the phase at which the images are captured via a camera. The device then converts the image into its digital form and is then fed to the AI image processor tool or platform. It’s the least technical stage of the process, as it only requires an imaging device like a scanner or digital camera.
- The enhancement makes the image look better or at least recognizable to the human eye. The idea behind it is to ensure details that are difficult to notice become more visible. AI/Machine learning practitioners may also perform image enhancement to highlight certain features that are of interest to the purpose of that particular project. For instance, an image captured by a self-driving car may highlight road signs, passengers, and boundaries. Common enhancement techniques include contrast modifications and spatial filtering.
- Restoration aims to “fix” issues concerning an image’s appearance. It’s different from enhancement, which aims to “improve” without fixing an issue. Image restoration may involve eliminating image noise and minimizing the quality loss from compression. An excellent example of restoration would be when you revitalize an image from the 80s.
- Color processing is when the AI tool manipulates, transforms, and analyzes the visual data of each color used in an image. It’s basically image processing, except it applies to each individual color. It aims to extract valuable data from the individual colors of an image to simplify the data extraction process for the entirety of the image.
- Wavelets are the representation of the image in different resolution scales. It helps AI/ML practitioners figure out the ideal configuration for their purposes. Wavelets are most commonly used to find the best settings for image compression to ensure the image has less storage space without compromising its quality, at least not significantly.
- Compression is when the AI image processor reduces the file size or the bandwidth of an image. It’s a crucial stage as it allows practitioners to store more images in specified disk space. In the previous key stage, wavelets, and compression go hand in hand.
- Morphological processing is the process of assigning a shape to each object.
- Segmentation utilizes the shapes generated from the previous step to separate small objects on an image from the background without compromising the size and shape of the rest of the image. Morphological processing and segmentation, too, go hand in hand.
- Representation and description is the stage where the image processing tool decides how it represents each of the constituent parts of the image. It chooses the representation that’s most suitable for the ensuing stage. Meanwhile, description is the process of describing an image according to its individual characteristics.
- Recognition assigns a label to each object on an image according to the info the AI image processing tool received from the results of representation and description.
Keep in mind that these ten fundamental stages apply both to AI image processing and regular image processing, though there may be slight differences. However, if you wish to utilize this technology for your AI or machine learning projects, having a full understanding of these stages is necessary.
Closing Thoughts
Implementing image processing for your AI projects is challenging enough as it is. Naturally, the level of difficulty would go higher if you wish to incorporate AI technology into it as well. That’s why you should at least have a basic understanding of the task at hand, and this brief guide is the perfect way to gain that understanding.