Image Denoising with Modified Grey Wolf Optimizer
Abstract
In this study, image denoising has been realized with with the one of the recent Nature-Inspired optimization algorithms, Grey Wolf Optimizer(GWO). GWO is one of the recent most studied continous optimization algorithm which performs better than the other algorithms. In this study, ten test images have been selected and gaussian noise has been added with some variance values. After the noisy images have been attained, these noisy images have been filtered with convulation in spatial domain. Filter coefficents have been trained with GWO, Modified Grey Wolf Optimizer(MGWO) and Genetic Algorithm(GA). Weiner filtering is also applied on the images for image denosing. The results show that Weiner Filter outperforms GWO trained filters on most of the images. MGWO performance is better then GWO and the results show that MGWO can also be used as an alternative method for image denoising. In the future studies, adaptive MGWO can be enhanced for much more succesfull image denoising process.
Keywords
References
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Details
Primary Language
English
Subjects
Engineering
Journal Section
Research Article
Publication Date
August 1, 2018
Submission Date
June 22, 2018
Acceptance Date
June 27, 2018
Published in Issue
Year 2018 Volume: 6 Number: 4
Cited By
Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN
Journal of the Chinese Institute of Engineers
https://doi.org/10.1080/02533839.2021.2012525