A Novel Image Denoising Algorithm Based on Non-subsampled Contourlet Transform and Modified NLM

  • Huayong Yang
  • Xiaoli Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)


A novel image denoising algorithm based on non-subsampled contourlet transform (NSCT) and modified non-local mean (NLM) is proposed. First, we utilize NSCT to decompose the images to obtain the high frequency coefficients. Second, the high frequency coefficients are used for modified NLM denoising. Finally, the NLM weight values are calculated by modified bisquare function instead of Gaussian kernel function of the traditional NLM, and each noise coefficient is corrected to get the denoised image. According to results of the simulation experiment, the denoising results of the proposed algorithm obtain higher peak signal-to-noise ratio (PSNR) and better retains structural information of image in subjective vision.


Non-subsampled contourlet transform (NSCT) Non-local mean (NLM) Denoising 



This work was supported in part by National Natural Science Foundation of China (No. 61502356), by Hubei Province Natural Science Foundation of China (No. 2018CFB526).


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information EngineeringCity College of Wuhan University of Science and TechnologyWuhanChina
  2. 2.School of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina

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