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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 824787, 14 pages
http://dx.doi.org/10.1155/2013/824787
Research Article

An Image Enhancement Method Using the Quantum-Behaved Particle Swarm Optimization with an Adaptive Strategy

1School of Computer & Software Engineering, Nanjing Institute of Industry Technology, Nanjing 210046, China
2School of IOT Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
3School of Information Engineering, Huzhou Teachers College, Huzhou, Zhejiang 313000, China

Received 25 January 2013; Accepted 22 April 2013

Academic Editor: Jui-Sheng Lin

Copyright © 2013 Xiaoping Su 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.

Abstract

Image enhancement techniques are very important to image processing, which are used to improve image quality or extract the fine details in degraded images. In this paper, two novel objective functions based on the normalized incomplete Beta transform function are proposed to evaluate the effectiveness of grayscale image enhancement and color image enhancement, respectively. Using these objective functions, the parameters of transform functions are estimated by the quantum-behaved particle swarm optimization (QPSO). We also propose an improved QPSO with an adaptive parameter control strategy. The QPSO and the AQPSO algorithms, along with genetic algorithm (GA) and particle swarm optimization (PSO), are tested on several benchmark grayscale and color images. The results show that the QPSO and AQPSO perform better than GA and PSO for the enhancement of these images, and the AQPSO has some advantages over QPSO due to its adaptive parameter control strategy.