Image processing systems are possibly one of the fastest evolving technologies in existence today. This is because the applications of image processing are virtually endless with critical applications in engineering and computer science.
In fact, processing images has become such a key driver of daily operations that the capacity to process tons and tons of visual data has long since exceeded human capabilities. This is why we have increasingly relied on machines to interpret and analyze images.
The application of artificial intelligence (AI) in image processing has unlocked considerable capabilities in facial recognition and authentication functionality. This again has far-reaching implications in privacy, security, governance, detection and recognition of objects and patterns in images and videos, and more.
The applications of image processing can be widely seen in diverse fields, such as, medical visualization, biometrics, autonomous vehicles, gaming, surveillance, law enforcement, and more. In this article, we will help you understand the concept of digital image processing and the role of AI in it. In case you are interested to know more about AI-enhanced digital image processing and its applications for your business, please reach out to 24/7 IT support.
Table of Contents
Image processing refers to the process of manipulating an image. This is generally done in order to enhance the quality of the image or to extract specific information from the image. Image processing can be done through both analog and digital image processing methods. As the name suggests, analog image processing is primarily used for analyzing physical photographs, or hard copies of images.
Digital image processing, on the other hand, can help users manipulate digital images or soft copies. Digital image processing is capable of capturing more than just visual data as an output. For instance, it can be used to glean information from photographs or other visual data, such as textual data, or data associated with features, characteristics and other content embedded within the image.
Through the tools of image processing, it is not possible to transform a physical image into a digital one, enhance it, analyze it, get information out of the image, improve on the image (through technical enhancements) and more. The process can even be extrapolated into video analysis through freeze frames.
Image processing can be essentially understood as a process where images are treated as two-dimensional signals that can be processed using predetermined signal processing methods. For more detailed resources on digital image processing, please refer to Managed Cloud Services.
Image processing can be an intense and involved process, but it is increasingly being automated to minimize the need for human intervention and delay.
The first step is to acquire the image for processing. This can be done through using optical scanners on physical objects or simply acquiring digital photos.
Analyze and use images that often have technical staining patterns, such as, data compression, image enhancement, and satellite photographs.
The final step comprises the results of processing based on the analysis of the image.
As already outlined above, image acquisition is the basic first step required to kick off image processing. This is often referred to as preprocessing. This step involves acquiring digital copies of the image from a source that generally comes from some sort of hardware, such as a camera or a scanner.
With surveillance cameras and video and image processing becoming an unavoidable part of daily life, companies have to go through processing mountains of visual data as part of their daily operations.
Once the image is acquired, the next step involves image enhancement. This can involve highlighting characteristics of the image so some of the information obfuscated within becomes legible. This step can also involve playing around with features such as brightness, contrast, hue, saturation, exposure etc.
For images that have lost some of their integrity or have suffered from quality deterioration, the process of image restoration can help markedly improve the appearance of the image. The process of image restoration generally makes use of certain mathematical or probabilistic models in order to compensate for the loss of information in the original image.
Color image processing is a core part of image processing. This makes use of several color modeling techniques to process digital images. This step has really gained popularity in the recent past thanks to the ubiquity of digital images on the internet. This step can be quickly applied to enhance the aesthetic appeal of images or simply to implement color correction.
The techniques of image compression and decompression lets users change the size and resolution of images. Compression, as the name suggests, is used for reduction in size and resolution, while decompression can be used to restore a compressed image to its original size and resolution.
Although not used quite as extensively, morphological processing allows for morphing images based on their inherent shapes. For a deeper understanding of the phases of digital image processing and more specifically, how each can be adapted to your specific business use case, please reach out to IT Outsourcing Services.
This is the step where newer technologies like AI are used most extensively. The essential step of image recognition allows users to identify specific characteristics of objects in images. AI-enabled image recognition makes use of techniques like object detection, object recognition, and segmentation. Image recognition software has improved markedly in accuracy in the recent past and has been applied to a wide range of security and privacy use cases.
During digital processing, an image is often segmented into regions. These regions are further represented and described in a form suitable for further processing. Representation, in this case, refers to the image’s characteristics and regional properties. Description, on the other hand, refers to the quantitative information extracted that can help distinguish one class of objects from another.
Post Courtesy: Nora Erspamer – Director of Digital Marketing at New Charter Technologies.