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Multi-resolution gray-level image enhancement using particle swarm optimization

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Abstract

This paper presents a multi-resolution method for gray-level image enhancement using Particle Swarm Optimization (PSO). The enhancement optimization procedure is a non-linear problem with various constraints. The proposed image enhancement algorithm (MGE-PSO) generates a whole pyramid of differently sized image in order to utilize more information for improvement process. In fact, MGE-PSO employs the ability of image pyramid to determine informative parts of an image for visual perception. When an image is downscaled, area of homogeneous regions is decreased and informative pixels of input image can be selected easier. The PSO uses averaged variance value of all pixels included in the informative and non-informative classes of each level in image pyramid to move through search space for finding the best intensity values of pixels to transfer maximum visual perception. Experimental results on Berkeley dataset demonstrate the superiority of the proposed MGE-PSO to other methods. Beside, detailed analysis of selection criterion used in PSO are available.

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Notes

  1. Complete source code, useful functions or appropriate explanations for implementation are available for some comparative methods [36].

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Computer Engineering Department, University of Kashan, Kashan, Iran

    Ali Mohammad Nickfarjam & Hossein Ebrahimpour-Komleh

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  1. Ali Mohammad Nickfarjam
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  2. Hossein Ebrahimpour-Komleh
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Correspondence to Ali Mohammad Nickfarjam.

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Nickfarjam, A.M., Ebrahimpour-Komleh, H. Multi-resolution gray-level image enhancement using particle swarm optimization. Appl Intell 47, 1132–1143 (2017). https://doi.org/10.1007/s10489-017-0931-2

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  • DOI: https://doi.org/10.1007/s10489-017-0931-2

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