Super-Resolution via Particle Swarm Optimization Variants

What is it about?

Super-resolution (SR) reconstructs a high-resolution (HR) image from a set of low-resolution (LR) pictures and restores an HR video from a group of neighboring LR frames. Optimization tries to overcome the image acquisition limitations, the ill-posed nature of the SR problem, to facilitate content visualization and scene recognition. Particle swarm optimization (PSO) is a superb optimization algorithm used for all sorts of problems despite its tendency to be stuck in local minima. To handle ill-posedness, different PSO variants (hybrid versions) have been proposed trying to explore factors such as the initialization of the swarm, insertion of a constriction coefficient, mutation operators, and the use of an inertia weight. Hybridization involves combining two (or more) techniques wisely such that the resultant algorithm contains the good characteristics of both (or all) the methods. Interesting hybridization techniques include many local and global search approaches. Results for the SR reconstruction of still and video images are presented for the PSO and the HPSO algorithms.

Featured Image

Photo by Tom Podmore on Unsplash

Photo by Tom Podmore on Unsplash

Why is it important?

Super-resolution, Image registration, Fusion Image restoration, Mosaicking, Motion estimation, Particle swarm optimization, High-resolution imaging, High-resolution video, remote sensing, image processing, computer vision

Perspectives