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A comprehensive review of image denoising in deep learning

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dc.citedby8
dc.contributor.authorJebur R.S.en_US
dc.contributor.authorZabil M.H.B.M.en_US
dc.contributor.authorHammood D.A.en_US
dc.contributor.authorCheng L.K.en_US
dc.contributor.authorid57214077047en_US
dc.contributor.authorid35185866500en_US
dc.contributor.authorid56121544200en_US
dc.contributor.authorid57188850203en_US
dc.date.accessioned2025-03-03T07:43:25Z
dc.date.available2025-03-03T07:43:25Z
dc.date.issued2024
dc.description.abstractDeep learning has gained significant interest in image denoising, but there are notable distinctions in the types of deep learning methods used. Discriminative learning is suitable for handling Gaussian noise, while optimization models are effective in estimating real noise. However, there is limited research that summarizes the different deep learning techniques for image denoising. This paper conducts a comprehensive review of techniques and methods used for image denoising and identifying challenges associated with existing approaches. In this paper, a comparative study of deep techniques is offered in image denoising. The study conducted a comprehensive review of 68 papers on image denoising published between 2018 and 2023, providing a detailed analysis of the field?s progress and methodologies over a period of 5 years. Through its literature review, the paper provides a comprehensive summary of image denoising in deep learning, including machine learning methods for image denoising, CNNs for image denoising, additive white noisy-image denoising, real noisy image denoising, blind denoising, hybrid noisy images, state- of-the-art methods for image denoising with deep learning, salt and pepper noise, non-linear filters for digital color images. The main objective of this paper is to provide a comprehensive overview of various approaches used for image denoising, each of which has been explored and developed based on individual research studies. The paper aims to discuss these approaches in a systematic and organized manner, comparing their strengths and weaknesses to provide insights for future research in the field. ? The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11042-023-17468-2
dc.identifier.epage58199
dc.identifier.issue20
dc.identifier.scopus2-s2.0-85180259213
dc.identifier.spage58181
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85180259213&doi=10.1007%2fs11042-023-17468-2&partnerID=40&md5=30b7fb6d9f481d94fb0d34b32f863eeb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36614
dc.identifier.volume83
dc.pagecount18
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleMultimedia Tools and Applications
dc.subjectDeep learning
dc.subjectGaussian noise (electronic)
dc.subjectLearning systems
dc.subjectBlind denoising
dc.subjectDeep learning
dc.subjectDiscriminative learning
dc.subjectGaussians
dc.subjectHybrid noisy image
dc.subjectLearning methods
dc.subjectLearning techniques
dc.subjectNoisy image
dc.subjectOptimization models
dc.subjectSalt-and-pepper noise
dc.subjectSalt and pepper noise
dc.titleA comprehensive review of image denoising in deep learningen_US
dc.typeArticleen_US
dspace.entity.typePublication
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