Image and video denoising using adaptive dual-tree discrete wavelet packets

Jingyu Yang, Yao Wang, Wenli Xu, Qionghai Dai

Research output: Contribution to journalArticle

Abstract

We investigate image and video denoising using adaptive dual-tree discrete wavelet packets (ADDWP), which is extended from the dual-tree discrete wavelet transform (DDWT). With ADDWP, DDWT subbands are further decomposed into wavelet packets with anisotropic decomposition, so that the resulting wavelets have elongated support regions and more orientations than DDWT wavelets. To determine the decomposition structure, we develop a greedy basis selection algorithm for ADDWP, which has significantly lower computational complexity than a previously developed optimal basis selection algorithm, with only slight performance loss. For denoising the ADDWP coefficients, a statistical model is used to exploit the dependency between the real and imaginary parts of the coefficients. The proposed denoising scheme gives better performance than several state-of-the-art DDWT-based schemes for images with rich directional features. Moreover, our scheme shows promising results without using motion estimation in video denoising. The visual quality of images and videos denoised by the proposed scheme is also superior.

Original languageEnglish (US)
Article number4801613
Pages (from-to)642-655
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume19
Issue number5
DOIs
StatePublished - May 2009

Fingerprint

Discrete wavelet transforms
Decomposition
Motion estimation
Computational complexity

Keywords

  • Anisotropic decomposition
  • Complex wavelet packets
  • Directional transform
  • Image denoising
  • Video denoising

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Media Technology

Cite this

Image and video denoising using adaptive dual-tree discrete wavelet packets. / Yang, Jingyu; Wang, Yao; Xu, Wenli; Dai, Qionghai.

In: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 19, No. 5, 4801613, 05.2009, p. 642-655.

Research output: Contribution to journalArticle

@article{e3e94d8d205143ebbfd6e8d0a7f03f18,
title = "Image and video denoising using adaptive dual-tree discrete wavelet packets",
abstract = "We investigate image and video denoising using adaptive dual-tree discrete wavelet packets (ADDWP), which is extended from the dual-tree discrete wavelet transform (DDWT). With ADDWP, DDWT subbands are further decomposed into wavelet packets with anisotropic decomposition, so that the resulting wavelets have elongated support regions and more orientations than DDWT wavelets. To determine the decomposition structure, we develop a greedy basis selection algorithm for ADDWP, which has significantly lower computational complexity than a previously developed optimal basis selection algorithm, with only slight performance loss. For denoising the ADDWP coefficients, a statistical model is used to exploit the dependency between the real and imaginary parts of the coefficients. The proposed denoising scheme gives better performance than several state-of-the-art DDWT-based schemes for images with rich directional features. Moreover, our scheme shows promising results without using motion estimation in video denoising. The visual quality of images and videos denoised by the proposed scheme is also superior.",
keywords = "Anisotropic decomposition, Complex wavelet packets, Directional transform, Image denoising, Video denoising",
author = "Jingyu Yang and Yao Wang and Wenli Xu and Qionghai Dai",
year = "2009",
month = "5",
doi = "10.1109/TCSVT.2009.2017402",
language = "English (US)",
volume = "19",
pages = "642--655",
journal = "IEEE Transactions on Circuits and Systems for Video Technology",
issn = "1051-8215",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "5",

}

TY - JOUR

T1 - Image and video denoising using adaptive dual-tree discrete wavelet packets

AU - Yang, Jingyu

AU - Wang, Yao

AU - Xu, Wenli

AU - Dai, Qionghai

PY - 2009/5

Y1 - 2009/5

N2 - We investigate image and video denoising using adaptive dual-tree discrete wavelet packets (ADDWP), which is extended from the dual-tree discrete wavelet transform (DDWT). With ADDWP, DDWT subbands are further decomposed into wavelet packets with anisotropic decomposition, so that the resulting wavelets have elongated support regions and more orientations than DDWT wavelets. To determine the decomposition structure, we develop a greedy basis selection algorithm for ADDWP, which has significantly lower computational complexity than a previously developed optimal basis selection algorithm, with only slight performance loss. For denoising the ADDWP coefficients, a statistical model is used to exploit the dependency between the real and imaginary parts of the coefficients. The proposed denoising scheme gives better performance than several state-of-the-art DDWT-based schemes for images with rich directional features. Moreover, our scheme shows promising results without using motion estimation in video denoising. The visual quality of images and videos denoised by the proposed scheme is also superior.

AB - We investigate image and video denoising using adaptive dual-tree discrete wavelet packets (ADDWP), which is extended from the dual-tree discrete wavelet transform (DDWT). With ADDWP, DDWT subbands are further decomposed into wavelet packets with anisotropic decomposition, so that the resulting wavelets have elongated support regions and more orientations than DDWT wavelets. To determine the decomposition structure, we develop a greedy basis selection algorithm for ADDWP, which has significantly lower computational complexity than a previously developed optimal basis selection algorithm, with only slight performance loss. For denoising the ADDWP coefficients, a statistical model is used to exploit the dependency between the real and imaginary parts of the coefficients. The proposed denoising scheme gives better performance than several state-of-the-art DDWT-based schemes for images with rich directional features. Moreover, our scheme shows promising results without using motion estimation in video denoising. The visual quality of images and videos denoised by the proposed scheme is also superior.

KW - Anisotropic decomposition

KW - Complex wavelet packets

KW - Directional transform

KW - Image denoising

KW - Video denoising

UR - http://www.scopus.com/inward/record.url?scp=67249103198&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67249103198&partnerID=8YFLogxK

U2 - 10.1109/TCSVT.2009.2017402

DO - 10.1109/TCSVT.2009.2017402

M3 - Article

VL - 19

SP - 642

EP - 655

JO - IEEE Transactions on Circuits and Systems for Video Technology

JF - IEEE Transactions on Circuits and Systems for Video Technology

SN - 1051-8215

IS - 5

M1 - 4801613

ER -