@article{article_435783, title={Image Denoising with Modified Grey Wolf Optimizer}, journal={Duzce University Journal of Science and Technology}, volume={6}, pages={962–982}, year={2018}, DOI={10.29130/dubited.435783}, author={Ardaç, Hüseyin Avni and Erdoğmuş, Pakize}, keywords={GWO,MGWO,GA,Gürültü Temizleme}, abstract={<p class="MsoNormal" style="margin-bottom:.0001pt;text-align:justify;"> <span style="font-size:10pt;line-height:115%;font-family:’Times New Roman’, serif;">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. </span> </p> <p> </p>}, number={4}, publisher={Düzce Üniversitesi}