Weighted Nuclear Norm Minimization Image Denoising Method Based on Noise Variance Estimation

  • Shujuan WangEmail author
  • Ying Liu
  • Hong Liang
  • Yanwei Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


Weighted nuclear norm minimization (WNNM) uses image non-local similarity to deal with image denoising; this method not only maintains the detailed texture edge structure but also reduces the impact on distortion of the image after denoising. However, WNNM method assumes that the noise variance of the image is known, where the parameter is set by subjective experience that will result in incompleteness in theory. To handle this issue, it is proposed to pre-estimate noise variance based on discrete wavelet transformation (DWT). The simulation result shows that compared with original WNNM method, pre-estimate noise variance in image denoising has a faster algorithm running speed and a higher image signal-to-noise ratio after denoising.


WNNM algorithm Discrete wavelet transformation Singular value decomposition Image denoising 



This work was supported by the Fundamental Research Funds for the Central Universities under Grant No. HEUCFP201802.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shujuan Wang
    • 1
    Email author
  • Ying Liu
    • 1
  • Hong Liang
    • 2
  • Yanwei Wang
    • 3
  1. 1.College of Science, Harbin Engineering UniversityHarbinChina
  2. 2.College of Automation, Harbin Engineering UniversityHarbinChina
  3. 3.College of Mechanical Engineering, Harbin Institute of PetroleumHarbinChina

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