An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm

Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is...

Full description

Saved in:
Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2015-01, Vol.2015 (2015), p.1289-1300
Main Authors: Liu, Wei, Hu, Zhengbing, Wang, Mingwei, Ye, Zhiwei
Format: Article
Language:English
Subjects:
Algorithms
Computer Simulation
Criteria
Distribution (Probability theory)
Entropy
Fourier transforms
Genetic algorithms
Image contrast
Image Enhancement
Image processing
Image Processing, Computer-Assisted
Image quality
Mathematical analysis
Mathematical models
Mathematical optimization
Methods
Models, Theoretical
Optimization algorithms
Search algorithms
Searching
Swarm intelligence
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.
ISSN:1687-5265
1687-5273
DOI:10.1155/2015/825398