An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm

Computational Intelligence and Neuroscience
Zhiwei YeWei Liu

Abstract

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. ...Continue Reading

Citations

Aug 11, 2020·Biomedizinische Technik. Biomedical Engineering·Ankit Vidyarthi

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Software Mentioned

BSAIE
windows XP
CS
GAIE
Matlab
Apurba
PSO
PSOIE

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