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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 920))

Abstract

Image denoising is one of the most important and critical issues in the field of digital image processing. Its purpose is to keep the edge and detail information as much as possible while removing the noise in the image. Numerous current denoising methods have poor performance when dealing with multiplicative noise, and they usually cannot well preserve the detail and edge information in the image. For this reason, we propose a Non-Local Means (NLM) based multiplicative denoising method. Firstly, the image is logarithmically transformed to convert the noise into multiplicative additive noise that is easier to handle. Secondly, a new square root kernel function is used to replace the original exponential kernel function for Non-Local Means filtering. Finally, the denoised image is obtained through the inverse exponential transformation. By comparing with a variety of classical denoising algorithms, we make the conclusions that the proposed method can not only effectively remove the multiplicative noise, but also better preserve the detail and edge information in the image, which will lay the foundation for more advanced image processing.

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Acknowledgements

This work was supported by the Hundred Talents Program of Chinese Academy of Sciences under grant No. Y9BEJ11001. This work was also supported by the innovation workstation of Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO) under grant No. E010210101.

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Correspondence to Xuguang Wang .

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Yu, B., Zhou, Y., Liu, X., Wang, X. (2022). A Non-Local Means Based Multiplicative Denoising Method for Image Processing. In: S. Shmaliy, Y., Abdelnaby Zekry, A. (eds) 6th International Technical Conference on Advances in Computing, Control and Industrial Engineering (CCIE 2021). CCIE 2021. Lecture Notes in Electrical Engineering, vol 920. Springer, Singapore. https://doi.org/10.1007/978-981-19-3927-3_75

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  • DOI: https://doi.org/10.1007/978-981-19-3927-3_75

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  • Online ISBN: 978-981-19-3927-3

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