Image denoising and enhancement based on adaptive wavelet thresholding and mathematical morphology

Yungang Zhang; Bailing Zhang; Wenjin Lu

doi:10.1109/CISP.2010.5647208

Image denoising and enhancement based on adaptive wavelet thresholding and mathematical morphology

Yungang Zhang^*, Bailing Zhang, Wenjin Lu

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

3 Citations (Scopus)

Abstract

Wavelet thresholding is an effective way of image denoising and enhancement. The most important issue in wavelet thresholding is how to find an optimal threshold. In this paper, an adaptive threshold selection technique is proposed and morphological operations to improve the denoised result are discussed. An image denoising and enhancement scheme based on the adaptive wavelet shrinkage and mathematical morphology is described. Compared with some existing denoising methods such as VisuShrinkage, BayesShrinkage, the experimental result shows the proposed method outperforms these techniques in terms of PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error).

Original language	English
Title of host publication	Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
Pages	693-697
Number of pages	5
DOIs	https://doi.org/10.1109/CISP.2010.5647208
Publication status	Published - 2010
Event	2010 3rd International Congress on Image and Signal Processing, CISP 2010 - Yantai, China Duration: 16 Oct 2010 → 18 Oct 2010

Publication series

Name	Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
Volume	2

Conference

Conference	2010 3rd International Congress on Image and Signal Processing, CISP 2010
Country/Territory	China
City	Yantai
Period	16/10/10 → 18/10/10

Keywords

Adaptive thresholding
Image denoising
Image enhancement
Mathematical morphology
Wavelet shrinkage

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10.1109/CISP.2010.5647208

Cite this

Zhang, Y., Zhang, B., & Lu, W. (2010). Image denoising and enhancement based on adaptive wavelet thresholding and mathematical morphology. In Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010 (pp. 693-697). Article 5647208 (Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010; Vol. 2). https://doi.org/10.1109/CISP.2010.5647208

@inproceedings{eedd86505b62421aa369a9bc0c629fb5,

title = "Image denoising and enhancement based on adaptive wavelet thresholding and mathematical morphology",

abstract = "Wavelet thresholding is an effective way of image denoising and enhancement. The most important issue in wavelet thresholding is how to find an optimal threshold. In this paper, an adaptive threshold selection technique is proposed and morphological operations to improve the denoised result are discussed. An image denoising and enhancement scheme based on the adaptive wavelet shrinkage and mathematical morphology is described. Compared with some existing denoising methods such as VisuShrinkage, BayesShrinkage, the experimental result shows the proposed method outperforms these techniques in terms of PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error).",

keywords = "Adaptive thresholding, Image denoising, Image enhancement, Mathematical morphology, Wavelet shrinkage",

author = "Yungang Zhang and Bailing Zhang and Wenjin Lu",

year = "2010",

doi = "10.1109/CISP.2010.5647208",

language = "English",

isbn = "9781424465149",

series = "Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010",

pages = "693--697",

booktitle = "Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010",

note = "2010 3rd International Congress on Image and Signal Processing, CISP 2010 ; Conference date: 16-10-2010 Through 18-10-2010",

}

Zhang, Y, Zhang, B & Lu, W 2010, Image denoising and enhancement based on adaptive wavelet thresholding and mathematical morphology. in Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010., 5647208, Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010, vol. 2, pp. 693-697, 2010 3rd International Congress on Image and Signal Processing, CISP 2010, Yantai, China, 16/10/10. https://doi.org/10.1109/CISP.2010.5647208

Image denoising and enhancement based on adaptive wavelet thresholding and mathematical morphology. / Zhang, Yungang; Zhang, Bailing; Lu, Wenjin.
Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010. 2010. p. 693-697 5647208 (Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010; Vol. 2).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Image denoising and enhancement based on adaptive wavelet thresholding and mathematical morphology

AU - Zhang, Yungang

AU - Zhang, Bailing

AU - Lu, Wenjin

PY - 2010

Y1 - 2010

N2 - Wavelet thresholding is an effective way of image denoising and enhancement. The most important issue in wavelet thresholding is how to find an optimal threshold. In this paper, an adaptive threshold selection technique is proposed and morphological operations to improve the denoised result are discussed. An image denoising and enhancement scheme based on the adaptive wavelet shrinkage and mathematical morphology is described. Compared with some existing denoising methods such as VisuShrinkage, BayesShrinkage, the experimental result shows the proposed method outperforms these techniques in terms of PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error).

AB - Wavelet thresholding is an effective way of image denoising and enhancement. The most important issue in wavelet thresholding is how to find an optimal threshold. In this paper, an adaptive threshold selection technique is proposed and morphological operations to improve the denoised result are discussed. An image denoising and enhancement scheme based on the adaptive wavelet shrinkage and mathematical morphology is described. Compared with some existing denoising methods such as VisuShrinkage, BayesShrinkage, the experimental result shows the proposed method outperforms these techniques in terms of PSNR (Peak Signal to Noise Ratio) and MSE (Mean Square Error).

KW - Adaptive thresholding

KW - Image denoising

KW - Image enhancement

KW - Mathematical morphology

KW - Wavelet shrinkage

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DO - 10.1109/CISP.2010.5647208

M3 - Conference Proceeding

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SN - 9781424465149

T3 - Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010

SP - 693

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BT - Proceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010

T2 - 2010 3rd International Congress on Image and Signal Processing, CISP 2010

Y2 - 16 October 2010 through 18 October 2010

ER -

Image denoising and enhancement based on adaptive wavelet thresholding and mathematical morphology

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