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Gaussian Noise Detection and Adaptive Non-local Means Filter

  • Peng Chen
  • Shiqian Wu
  • Hongping Fang
  • Bin Chen
  • Wei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10749)

Abstract

In this paper, a noise adaptive non-local means (NA-NLM) filter is presented to remove additive Gaussian noise from the corrupted images. Firstly, a novel pixel-wise Gaussian noise detection is proposed via eigen features of local Hessian matrix, and a metric is introduced to measure noise strength. Then, image denoising is performed by adaptive NLM filter according to the pixel-wise noise strength, i.e., the NLM filter varies adaptively with the size selections of the search window and similar patches. Experiments carried on Tampere Image Database (TID) demonstrate that the proposed method outperforms the state-of-the-art methods in terms of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and subjective visual assessment.

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61371190.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Peng Chen
    • 1
  • Shiqian Wu
    • 1
  • Hongping Fang
    • 2
  • Bin Chen
    • 2
  • Wei Wang
    • 1
  1. 1.Key Laboratory of Metallurgical Equipment and Control TechnologyMinistry of EducationWuhanChina
  2. 2.School of Information Science and TechnologyWuhan University of Science and TechnologyWuhanChina

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