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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1166))

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Abstract

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.

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Funding

The research work was supported by the open project of State Key Laboratory of Millimeter Waves (Grant No. K202218). The author declares there is no conflict of interest regarding this paper.

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Correspondence to Shuwen Chen or Yudong Zhang .

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Wu, M., Wang, S., Chen, S., Zhang, Y. (2024). Machine Learning for Image Denoising: A Review. In: Su, R., Zhang, YD., Frangi, A.F. (eds) Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023). MICAD 2023. Lecture Notes in Electrical Engineering, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-97-1335-6_30

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  • DOI: https://doi.org/10.1007/978-981-97-1335-6_30

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