Biologically Rationalized Computing Techniques For Image Processing Applications

de Jesus, Maria Aparecida, de Jesus, Maria A., de Jesus, M. A., Vania Vieira Estrela, Saotome, O., Saotome, Osamu, Stutz, Dalmo, Vania Vieira Estrela, Vania Vieira Estrela, Vieira Estrela, Vania, others. Biologically Rationalized Computing Techniques For Image Processing Applications. In Biologically Rationalized Computing Techniques For Image Processing Applications. Volume 25 of pages 317-337, Lecture Notes in Computational Vision and Biomechanics (LNCVB), Springer Cham, Online ISBN 978-3-319-61316-1, Print ISBN 978-3-319-61315-4, doi: 10.1007/978-3-319-61316-1\_14, https://link.springer.com/chapter/10.1007/978-3-319-61316-1\_14, 2017. [doi]

Abstract

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.