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Image denoising techniques: An overview

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dc.citedby2
dc.contributor.authorJebur R.S.en_US
dc.contributor.authorDer C.S.en_US
dc.contributor.authorHammood D.A.en_US
dc.contributor.authorWeng L.Y.en_US
dc.contributor.authorid57214077047en_US
dc.contributor.authorid58510587900en_US
dc.contributor.authorid56121544200en_US
dc.contributor.authorid26326032700en_US
dc.date.accessioned2024-10-14T03:18:07Z
dc.date.available2024-10-14T03:18:07Z
dc.date.issued2023
dc.description.abstractThis article provides an in-depth overview of image denoising. Furthermore, the technical aspects developed for image denoising are highlighted in a wider sense. Image denoising is the removal of noise from a noisy image. Most importantly, one has to keep track of the information on image details. The challenges of image denoising, on the other hand, have not improved significantly. A general review for the image denoising mechanisms will be presented. Those mechanisms contain more than one filter like Baysion, Mean, Median, Gaussian, Guide, as well as collaborative filters along with various noise kinds like salt and peppers, speckle, Gaussian, and realistic noise. Each one has pros and cons. There exist mechanisms for denoising images which are developed and improved using ANN, CNN, AI, fuzzy algorithms and Coccuo search. To reduce noise and improve image quality, many techniques have been developed, including wavelet threshold-based strategies, linear and nonlinear filters. The majority of existing mechanisms are not trying to mitigate the multiple noise effects. This work discusses these noise techniques and types. � 2023 American Institute of Physics Inc.. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo20002
dc.identifier.doi10.1063/5.0154497
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85176739555
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85176739555&doi=10.1063%2f5.0154497&partnerID=40&md5=7da7b8550fb02e2ed8388412fbe9161f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34138
dc.identifier.volume2804
dc.publisherAmerican Institute of Physics Inc.en_US
dc.sourceScopus
dc.sourcetitleAIP Conference Proceedings
dc.subjectAI
dc.subjectImage Denoising
dc.subjectPSNR and SSIM
dc.titleImage denoising techniques: An overviewen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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