Machine Learning for Image Denoising: A Review

Meng Wu, Shuihua Wang, Shuwen Chen*, Yudong Zhang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Due to the increasing demand for high-quality images in various applications, more attention is paid to image denoising to improve image quality. However, the traditional image-denoising methods have great limitations for complex noise patterns. Machine learning (ML), especially deep learning (DL), has been given attention to solving this problem and has a good prospect for image denoising. This paper raises the necessity of image denoising first, then some common noise types are introduced, and finally, the technology of denoising— ML is discribed. The combined use of image-denoising methods can figure out complex situations to a large extent, providing a guarantee for high-quality images. Since DL can automatically learn complex patterns and levels from data, DL architectures such as CNNs and GANs are used to denoise. We can determine the quality of denoising results by evaluation metrics that provide quantitative measures, such as PSNR and MSE. This paper comprehensively considers the effectiveness of ML and DL in image denoising, and affirms its future potential.

Original languageEnglish
Title of host publicationProceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023) - Medical Imaging and Computer-Aided Diagnosis
EditorsRuidan Su, Yu-Dong Zhang, Alejandro F. Frangi
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9789819713349
Publication statusPublished - 2024
EventInternational Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2023 - Cambridge, United Kingdom
Duration: 9 Dec 202310 Dec 2023

Publication series

NameLecture Notes in Electrical Engineering
Volume1166 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


ConferenceInternational Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2023
Country/TerritoryUnited Kingdom


  • Deep learning architectures
  • Evaluation metrics
  • Machine learning


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