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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 373425, 13 pages
Research Article

Video Superresolution via Parameter-Optimized Particle Swarm Optimization

School of Aerospace Science and Technology, Xidian University, Xi’an 710071, China

Received 19 April 2014; Revised 15 July 2014; Accepted 7 August 2014; Published 28 August 2014

Academic Editor: Antonio Ruiz-Cortes

Copyright © 2014 Yunyi Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Video superresolution (VSR) aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO). We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS-PSO as an optimization method to solve the VSR problem, which overcomes the poor effect, low accuracy, and large calculation cost in other VSR algorithms. The proposed VSR method does not require exact movement estimation and does not need the computation of movement vectors. In terms of peak signal-to-noise ratio (PSNR), sharpness, and entropy, the proposed VSR method based POMS-PSO showed better objective performance. Besides objective standard, experimental results also proved the proposed method could reconstruct high-resolution video sequence with better subjective quality.