Image enhancement technique based on improved PSO algorithm

Qinqing, Gao; Chen, Dexin; Zeng, Guangping; He, Ketai

doi:10.1109/iciea.2011.5975586

Cited by 34 publications

(13 citation statements)

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“…Particle swarm optimization (PSO) is a new method of optimization developed in 1995 by Eberhart and Kennedy [7]. The idea of PSO was inspired by social behavior of bird flocking, fish schooling, as well as swarming theory.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Many authors explored the use of Particle Swarm Optimization (PSO) to solve variety of problems in computer science and engineering [3,4]. The application of PSO in image enhancement was presented in [5][6][7]. In [5], the author used PSO to solve the image enhancement problem by maximizing an objective fitness criterion in order to enhance contrast and detail in the image by adapting the parameters of a novel extension to a local enhancement technique.…”

Section: Introductionmentioning

confidence: 99%

“…In [5], the author used PSO to solve the image enhancement problem by maximizing an objective fitness criterion in order to enhance contrast and detail in the image by adapting the parameters of a novel extension to a local enhancement technique. In [7], the authors proposed an enhancement technology of gray image where image enhancement is considered as an optimization problem and PSO is used to solve it. The author improved the quality of the intensity image by a parameterized transformation function, in which parameters are optimized by PSO based on an objective function.…”

Section: Introductionmentioning

confidence: 99%

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Application of Particle Swarm Optimization in Histogram Equalization for image enhancement

Masra

Peilin

Muhammad

et al. 2012

2012 IEEE Colloquium on Humanities, Science and Engineering (CHUSER)

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Abstract-The basic and most common technique for contrast adjustment in the image is using Histogram Equalization (HE). It is based on equalizing the histogram of the image and subsequently enhancing its contrast, and results in overall contrast improvement. This paper introduces a combination of normal HE technique and Particle Swarm Optimization (PSO) algorithm for enhancing distorted image naturally. The process is as follows. The image is separated into red, green and blue (RGB) channels and PSO algorithm is applied into each channel in order to get its best fitness value. The fitness value that is obtained then will be applied into HE's normalization, after that the processed color image will be merged back to RGB image. Experimental results have shown the effectiveness in improving the contrast of the original images without introducing disturbing artifacts caused by normal HE.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Particle Swarm Optimization in Histogram Equalization for image enhancement

Masra

Peilin

Muhammad

et al. 2012

2012 IEEE Colloquium on Humanities, Science and Engineering (CHUSER)

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“…Human like system are required as the human systems are adaptable to change and under unknown circumstances. Soft computing differs from conventional computing [15]. Although Soft computing has various advantages it suffers from the problem of large computations.…”

Section: Introductionmentioning

confidence: 99%

“…System used for solving real world problem requires intelligence and be able to take decision and give reasons to it. Soft computing has several constituents [15]. The principal constituents of Soft Computing (SC) are .…”

Section: Introductionmentioning

confidence: 99%

Comparison of Traditional Approach for Edge Detection with Soft Computing Approach

Joshi¹,

Choubey²

2014

IJCA

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Image processing supports applications in different fields such as medicine, astronomy, product quality, industrial applications. Edge detection plays important role in segmentation and object identification process. Soft computing approach represents a good mathematical framework to deal with uncertainty of information. The performance of the well-known edge detectors, like Canny, Sobel, etc, depends critically on the choice of the input parameters. Threshold decision is the key uncertainty in the edge detection algorithms. In this paper, an improved edge detection algorithm based on fuzzy combination of mathematical morphology and multiscale wavelet transform is proposed. The proposed method overcomes the limitation of wavelet based edge detection and mathematical morphology based edge detection in noisy images. Method present will give best results for noisy images.

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