An image denoising method based on nonsubsampled contourlet transform with SQP optimization

Chen Yang, Yaozhong Yu, Qingdong Li, Xiwang Dong, Zhang Ren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this paper, we propose an image denoising method based on nonsubsampled contourlet transform (NSCT) with successive quadratic programming (SQP) optimization. This method can obtain the optimal threshold for each subband without the priori information of the noise variance using SQP optimization and generalized cross validation (GCV) criterion. After the threshold is determined, a nonlinear threshold function is applied to overcome the inadequate of soft threshold and hard threshold function. The experimental results show that the proposed method has a better performance than other contourlet-based image denoising methods and outperforms on both visual quality and peak signal-to-noise ratio (PSNR).

Original languageEnglish
Title of host publicationProceedings of the 36th Chinese Control Conference, CCC 2017
EditorsTao Liu, Qianchuan Zhao
PublisherIEEE Computer Society
Pages5455-5459
Number of pages5
ISBN (Electronic)9789881563934
DOIs
Publication statusPublished - 7 Sept 2017
Externally publishedYes
Event36th Chinese Control Conference, CCC 2017 - Dalian, China
Duration: 26 Jul 201728 Jul 2017

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference36th Chinese Control Conference, CCC 2017
Country/TerritoryChina
CityDalian
Period26/07/1728/07/17

Keywords

  • Image denoising
  • generalized cross validation
  • nonlinear threshold function
  • nonsubsampled contourlet transform
  • successive quadratic programming

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