Swarm Algorithms and (Better) Digital Photos

On the heels of my recent note about photography, digital cameras, and embedded endoscopes (aka "cameras"), it turns out that researchers are turning to "swarm intelligence" as a means of enhancing digital photos. Swarm intelligence is an AI technique that's based on the collective behavior of decentralized, self-organized systems.

The technique that Malik Braik, Alaa Sheta , and Aladdin Ayesh have developed uses a Particle Swarm Optimization (PSO) algorithm to enhance contrast and details without distortion.

Somewhat like Genetic Algorithms, PSO treats each version of an image as an individual member of a 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.

In their paper Particle Swarm Optimisation Enhancement Approach for Improving Image Quality, the authors present image enhancement as an optimization problem to which PSO is applied. This application is done within a nouvelle automatic image enhancement technique encompassing a real-coded particle swarms algorithm. The enhancement process is a non-linear optimization problem with several constraints. According to the researchers, "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."

-- Jonathan Erickson
jerickson@ddj.com

Posted by Jon Erickson at 11:14 AM  Permalink