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- W2135017002 abstract "This paper proposes a new variant of Particle Swarm Optimization (PSO) called Accelerated Particle Swarm Optimization (APSO) in gray level image enhancement application. Image enhancement is mainly done by maximizing the information content of the enhanced image with intensity transformation function. In this paper image enhancement is considered as an optimization problem and APSO is used to solve it. APSO is simpler to implement and it has faster convergence when compared to the PSO algorithm. Hence as an alternative to PSO based image enhancement algorithm, APSO is introduced in this present paper. In this 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 have achieved the best enhanced image according to the objective criterion by optimizing the parameters used in the transformation function with the help of APSO. The enhancement is done using three techniques: Histogram equalization (HE), Contrast stretching (LCS) and APSO. Different gray level images are taken and processed through these techniques, simulated in MATLAB. Results obtained using all these techniques are in good agreement and are compared using performance graphs and image based enhancement results. Simulation result proves that APSO based image enhancement algorithm is superior to the traditional techniques." @default.
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- W2135017002 date "2015-03-31" @default.
- W2135017002 modified "2023-09-23" @default.
- W2135017002 title "Image Enhancement using Accelerated Particle Swarm Optimization" @default.
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- W2135017002 doi "https://doi.org/10.17577/ijertv4is031075" @default.
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