NDLI: Effective hybrid genetic algorithm for removing salt and pepper noise

Content Provider	IET Digital Library
Author	Alaoui, Nail Mitiche, Amel Baha Houda Adamou Mitiche, Lahcène
Abstract	This study presents a new approach for recovering an image perturbed by salt and pepper noise (SPN) using a hybrid genetic algorithm (HGA) at all densities, called effective HGA (EHGA). The main contribution of the proposed algorithm is combining the genetic algorithm with image denoising methods that are integrated into the population to achieve rapid convergence. The idea is to evolve a group of individuals into a number of iterations using crossover and mutation operators. This approach evolves a set of images rather than a set of parameters from the filters. Experimental results of simulation on different images using peak signal-to-noise ratio, structural similarity index metric, image enhancement factor and Universal Quality Index show that the proposed algorithm outperforms other methods in removing the SPN qualitatively and quantitatively if the noise density is moderate and high. EHGA also preserves important features such as texture and corners of the image.
Starting Page	289
Ending Page	296
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 2, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0566
Journal	IET Image Processing
Publisher Date	2019-10-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Called Effective HGA Computer Vision And Image Processing Technique Effective Hybrid Genetic Algorithm EHGA Genetic Algorithm Image Denoising Image Denoising Method Image Enhancement Image Enhancement Factor Mutation Operators Noise Density Optical, Image And Video Signal Processing Optimisation Technique Peak Signal-to-noise Ratio Pepper Noise Rapid Convergence Structural Similarity Index Metric
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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