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Proceedings ArticleDOI

Gray-level Image Enhancement By Particle Swarm Optimization

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TLDR
Image enhancement is considered as an optimization problem and PSO is used to solve it and an objective criterion for measuring image enhancement is used which considers entropy and edge information of the image.
Abstract
Particle Swarm Optimization (PSO) algorithms represent a new approach for optimization. In this paper image enhancement is considered as an optimization problem and PSO is used to solve it. Image enhancement is mainly done by maximizing the information content of the enhanced image with intensity transformation function. In the present work a parameterized transformation function is used, which uses local and global information of the image. Here an objective criterion for measuring image enhancement is used which considers entropy and edge information of the image. We tried to achieve the best enhanced image according to the objective criterion by optimizing the parameters used in the transformation function with the help of PSO. Results are compared with other enhancement techniques, viz. histogram equalization, contrast stretching and genetic algorithm based image enhancement.

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Citations
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Journal ArticleDOI

An artificial bee colony algorithm for image contrast enhancement

TL;DR: A new Artificial Bee Colony (ABC) algorithm for image contrast enhancement is proposed, using a grey-level mapping technique and a new image quality measure, and the comparisons of the obtained results with the genetic algorithm have proven its superiority.
Journal ArticleDOI

A Survey on Nature-Inspired Optimization Algorithms and Their Application in Image Enhancement Domain

TL;DR: This study presents an up-to-date review over the application of NIOAs in image enhancement domain and the key issues which are involved in the formulation of NioAs based image enhancement models are discussed here.
Journal ArticleDOI

Particle swarm optimized multi-objective histogram equalization for image enhancement

TL;DR: A multi-objective HE model has been proposed in order to enhance the contrast as well as to preserve the brightness and is proved to have an edge over the other contemporary methods in terms of entropy and contrast improvement index.
Journal ArticleDOI

Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach

TL;DR: A CS based approach has superior convergence and fitness values compared to PSO as the CS converge faster that proves the efficacy of the CS based technique and Image Quality Analysis (IQA) justifies the robustness of the proposed enhancement technique.
Journal ArticleDOI

Biologically inspired image enhancement based on Retinex

TL;DR: A learning strategy to select the optimal parameters of the nonlinear stretching by optimizing a novel image quality measurement, named as the Modified Contrast-Naturalness-Colorfulness (MCNC) function, which employs a more effective objective criterion and can better agree with human visual perception.
References
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Proceedings ArticleDOI

A new optimizer using particle swarm theory

TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
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TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.
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TL;DR: The Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to EC fundamentals, models, algorithms and applications, intended to become the standard reference resource for the evolutionary computation community.
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TL;DR: Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration into the adaptive mechanisms that enable intelligent behaviour in complex and changing environments, encompassing swarm intelligence, fuzzy systems, artificial neutral networks, artificial immune systems and evolutionary computation.
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