Image Denoising via Fast and Fuzzy Non-local Means Algorithm


Junrui Lv, Xuegang Luo, Journal of Information Processing Systems Vol. 15, No. 5, pp. 1108-1118, Oct. 2019  

https://doi.org/10.3745/JIPS.02.0122
Keywords: Fuzzy Metric, Image Denoising, Non-local Means Algorithm, Visual Similarity
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Abstract

Non-local means (NLM) algorithm is an effective and successful denoising method, but it is computationally heavy. To deal with this obstacle, we propose a novel NLM algorithm with fuzzy metric (FM-NLM) for image denoising in this paper. A new feature metric of visual features with fuzzy metric is utilized to measure the similarity between image pixels in the presence of Gaussian noise. Similarity measures of luminance and structure information are calculated using a fuzzy metric. A smooth kernel is constructed with the proposed fuzzy metric instead of the Gaussian weighted L2 norm kernel. The fuzzy metric and smooth kernel computationally simplify the NLM algorithm and avoid the filter parameters. Meanwhile, the proposed FMNLM using visual structure preferably preserves the original undistorted image structures. The performance of the improved method is visually and quantitatively comparable with or better than that of the current state-ofthe- art NLM-based denoising algorithms.


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Cite this article
[APA Style]
Lv, J. & Luo, X. (2019). Image Denoising via Fast and Fuzzy Non-local Means Algorithm. Journal of Information Processing Systems, 15(5), 1108-1118. DOI: 10.3745/JIPS.02.0122.

[IEEE Style]
J. Lv and X. Luo, "Image Denoising via Fast and Fuzzy Non-local Means Algorithm," Journal of Information Processing Systems, vol. 15, no. 5, pp. 1108-1118, 2019. DOI: 10.3745/JIPS.02.0122.

[ACM Style]
Junrui Lv and Xuegang Luo. 2019. Image Denoising via Fast and Fuzzy Non-local Means Algorithm. Journal of Information Processing Systems, 15, 5, (2019), 1108-1118. DOI: 10.3745/JIPS.02.0122.