Digital/computational image processing (which we will refer to simply as image processing from this point forward) has become so popular in the modern era that it exists in numerous aspects in our everyday life. Image processing and manipulation is involved when you take a picture with your camera or phone using different filters, or when advanced image editing software such as Adobe Photoshop is used, or even when you simply edit images using Microsoft Paint.

Many of the techniques and algorithms used in image processing were developed in the early 1960s for various purposes such as medical imaging, satellite image analysis, character recognition, and so on. However, these image processing techniques required significant computing power, and the fact that the available computer equipment at the time was unable to accommodate the need for fast number-crunching slowed down the use of image processing.

Fast-forwarding to the future, where powerful computers with fast, multicore processors were developed, image processing techniques consequently became much more accessible, and research on image processing increased significantly. Nowadays, numerous image processing applications are being actively developed and studied, including pattern recognition, classification, feature extraction, and so on. Specific image processing techniques, which take advantage of concurrent and parallel programming and would otherwise be extremely computationally time-consuming, include Hidden Markov models, independent component analysis, and even up-and-coming neural network models: