Super Resolution Reconstruction of Video Images Based on Improved Glowworm Swarm Optimization Algorithm

Abstract

Aiming at the pixel correlation of continuous multi frame image sequences in video super-resolution reconstruction, an effective video image super-resolution reconstruction model is established, which is transformed into optimization problem from pixel sequence of low-resolution image to pixel sequence of high-resolution image. As the basic Glowworm Swarm Optimization (GSO) algorithm is easy to fall into the extreme value oscillation and the local optimum, the global optimal individual impact factor and the local optimal individual impact factor are introduced in the location update strategy, the volatilization and gain coefficient of firefly fluorescein are improved, and an Improved Glowworm Swarm Optimization (IGSO) algorithm is proposed. Combined with the characteristics of super-resolution reconstruction, the algorithm's swarm input, firefly's luciferase and location update equation are redefined, the optimization objective function criterion is set. A set of video super-resolution reconstruction example verifies the feasibility and effectiveness of the proposed model and algorithm.