Image Denoising via Fast and Fuzzy Non-local Means Algorithm
2
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
Non-local means (NLM) algorithm is an effective and successful denoising method, but it is computationallyheavy. To deal with this obstacle, we propose a novel NLM algorithm with fuzzy metric (FM-NLM) for imagedenoising in this paper. A new feature metric of visual features with fuzzy metric is utilized to measure thesimilarity between image pixels in the presence of Gaussian noise. Similarity measures of luminance andstructure information are calculated using a fuzzy metric. A smooth kernel is constructed with the proposedfuzzy metric instead of the Gaussian weighted L2 norm kernel. The fuzzy metric and smooth kernelcomputationally simplify the NLM algorithm and avoid the filter parameters. Meanwhile, the proposed FMNLMusing visual structure preferably preserves the original undistorted image structures. The performance ofthe improved method is visually and quantitatively comparable with or better than that of the current state-ofthe-art NLM-based denoising algorithms.
Abstract
Non-local means (NLM) algorithm is an effective and successful denoising method, but it is computationallyheavy. To deal with this obstacle, we propose a novel NLM algorithm with fuzzy metric (FM-NLM) for imagedenoising in this paper. A new feature metric of visual features with fuzzy metric is utilized to measure thesimilarity between image pixels in the presence of Gaussian noise. Similarity measures of luminance andstructure information are calculated using a fuzzy metric. A smooth kernel is constructed with the proposedfuzzy metric instead of the Gaussian weighted L2 norm kernel. The fuzzy metric and smooth kernelcomputationally simplify the NLM algorithm and avoid the filter parameters. Meanwhile, the proposed FMNLMusing visual structure preferably preserves the original undistorted image structures. The performance ofthe improved method is visually and quantitatively comparable with or better than that of the current state-ofthe-art NLM-based denoising algorithms.
참고문헌 (15)
- P. Milanfar, "A tour of modern image filtering : new insights and methods, both practical and theoretical", IEEE Signal Processing Magazine, 30(1), pp.106~128 (2013)
- A. Buades, "A review of image denoising algorithms, with a new one", Multiscale Modeling & Simulation, 4(2), pp.490~530 (2005)
- X. G. Luo, "Fast nonlocal means image denoising algorithm using selective calculation", Journal of University of Electronic Science and Technology of China, 44(1), pp.84~90 (2015)
- H. Li, "A novel non-local means image denoising method based on grey theory", Pattern Recognition, 49, pp.237~248 (2016)
- V. May, "An algorithm for improving non-local means operators via lowrank approximation", IEEE Transactions on Image Processing, 25(3), pp.1340~1353 (2016)
- J. V. Manjon, "MRI denoising using non-local means", Medical Image Analysis, 12(4), pp.514~523 (2008)
- P. Q. Yin, "An improved non-local means image de-noising algorithm using Mahalanobis distance", Journal of Computer-Aided Design & Computer Graphics, 28(3), pp.404~410 (2016)
- S. Morillas, "A fast impulsive noise color image filter using fuzzy metrics", Real-Time Imaging, 11(5-6), pp.417~428 (2005)
- V. Gregori, "Examples of fuzzy metrics and applications", Fuzzy Sets and Systems, 170(1), pp.95~111 (2011)
- S. Grecova, "Perceptual similarity between color images using fuzzy metrics", Journal of Visual Communication and Image Representation, 34(230-235) (2016)
- S. Morillas, "Image Analysis and Recognition", pp.138~145 (2006), Springer
- K. Zhang, "Single image super-resolution with non-local means and steering kernel regression", IEEE Transactions on Image Processing, 21(11), pp.4544~4556 (2012)
- X. Chen, "Fast patch-based denoising using approximated patch geodesic paths", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1211~1218 (2013)
- K. Dabov, "Image denoising by sparse 3-D transform-domain collaborative filtering", IEEE Transactions on Image Processing, 16(8), pp.2080~2095 (2007)
- Z. Wang, "Image quality assessment : from error visibility to structural similarity", IEEE Transactions on Image Processing, 13(4), pp.600~612 (2004)