Image Denoising via Fast and Fuzzy Non-local Means Algorithm

  • Lv, Junrui (School of Computer Science and Engineering, Panzhihua University) ;
  • Luo, Xuegang (School of Computer Science and Engineering, Panzhihua University)
  • Received : 2018.01.03
  • Accepted : 2019.07.29
  • Published : 2019.10.31


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 FM-NLM 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-of-the-art NLM-based denoising algorithms.


Fuzzy Metric;Image Denoising;Non-local Means Algorithm;Visual Similarity


Supported by : Innovation Foundation (Believe in Engineering) of Sichuan Province of China


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