Well, not quite yet. Researchers in the UK and Jordan have been working on a new method of image enhancement using the Swarm approach. PSO (Particle Swarm Optimization) uses a computer algorithm that can intelligently boost contrast and detail in an image without distorting the underlying features. The current application being to help those pesky traffic cameras read your license plate better. If the technology ever disseminates to consumer cameras and printers, like our LightJet, the possibilities could be endless.
There have been several approaches to image enhancement developed by image manipulation software companies and others. However, none comes up to the standards of the kind of image enhancement often seen in fiction, where a blurry distorted image on a screen is rendered pin-sharp at the click of a mouse. PSO, however, takes image enhancement a step closer to this ideal.
PSO is based on a mathematical model of the social interactions of swarms. The algorithm treats each version of an image as an individual member of the swarm and makes a single, small adjustment to contrast levels, edge sharpness, and other image parameters. The algorithm then determines whether the new members of the swarm are better or worse than the original according to an objective fitness criterion.
“The objective of the algorithm is to maximize the total number of pixels in the edges, thus being able to visualize more details in the images,” explain the researchers. Such enhancement might be useful in improving snapshots of CCTV quality for identification of individuals or vehicle number plates, it might also have application in improving images produced with lower quality cameras, such as camera phones, that are required for use in publishing or TV where image quality standards are usually higher.
The process of enhancing step by step is repeated to create a swarm of images in computer memory which have been graded relative to each other, the fittest end up at the front of the swarm until a single individual that is the most effectively enhanced.
“The obtained results using grey scale images indicate that PSO is better than other approaches in terms of the computational time and both the objective evaluation and maximization of the number of pixels in the edges of the tested images,” they add.
This research was published recently in Inderscience’s International Journal of Innovative Computing and Applications.