Improving performance of wavelet-based image denoising algorithm using complex diffusion process

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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Improving performance of wavelet-based image denoising algorithm using complex diffusion process. / Nadernejad, Ehsan; Sharifzadeh, Sara; Korhonen, Jari.

In: Imaging Science Journal, Vol. 60, No. 4, 2012, p. 208-218.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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Nadernejad, Ehsan; Sharifzadeh, Sara; Korhonen, Jari / Improving performance of wavelet-based image denoising algorithm using complex diffusion process.

In: Imaging Science Journal, Vol. 60, No. 4, 2012, p. 208-218.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

Bibtex

@article{372e8df8eb5b4bcfa1ea5f2df7745db7,
title = "Improving performance of wavelet-based image denoising algorithm using complex diffusion process",
publisher = "Maney Publishing",
author = "Ehsan Nadernejad and Sara Sharifzadeh and Jari Korhonen",
year = "2012",
doi = "10.1179/1743131X11Y.0000000024",
volume = "60",
number = "4",
pages = "208--218",
journal = "Imaging Science Journal",
issn = "1368-2199",

}

RIS

TY - JOUR

T1 - Improving performance of wavelet-based image denoising algorithm using complex diffusion process

A1 - Nadernejad,Ehsan

A1 - Sharifzadeh,Sara

A1 - Korhonen,Jari

AU - Nadernejad,Ehsan

AU - Sharifzadeh,Sara

AU - Korhonen,Jari

PB - Maney Publishing

PY - 2012

Y1 - 2012

N2 - Image enhancement and de-noising is an essential pre-processing step in many image processing algorithms. In any image de-noising algorithm, the main concern is to keep the interesting structures of the image. Such interesting structures often correspond to the discontinuities (edges). In this paper, we present a new algorithm for image noise reduction based on the combination of complex diffusion process and wavelet thresholding. In the existing wavelet thresholding methods, the noise reduction is limited, because the approximate coefficients containing the main information of the image are kept unchanged. Since noise affects both the approximate and detail coefficients, the proposed algorithm for noise reduction applies the complex diffusion process on the approximation band in order to alleviate the deficiency of the existing wavelet thresholding methods. The algorithm has been examined using a variety of standard images and its performance has been compared against several de-noising algorithms known from the prior art. Experimental results show that the proposed algorithm preserves the edges better and in most cases, improves the measured visual quality of the denoised images in comparison to the existing methods known from the literature. The improvement is obtained without excessive computational cost, and the algorithm works well on a wide range of different types of noise.

AB - Image enhancement and de-noising is an essential pre-processing step in many image processing algorithms. In any image de-noising algorithm, the main concern is to keep the interesting structures of the image. Such interesting structures often correspond to the discontinuities (edges). In this paper, we present a new algorithm for image noise reduction based on the combination of complex diffusion process and wavelet thresholding. In the existing wavelet thresholding methods, the noise reduction is limited, because the approximate coefficients containing the main information of the image are kept unchanged. Since noise affects both the approximate and detail coefficients, the proposed algorithm for noise reduction applies the complex diffusion process on the approximation band in order to alleviate the deficiency of the existing wavelet thresholding methods. The algorithm has been examined using a variety of standard images and its performance has been compared against several de-noising algorithms known from the prior art. Experimental results show that the proposed algorithm preserves the edges better and in most cases, improves the measured visual quality of the denoised images in comparison to the existing methods known from the literature. The improvement is obtained without excessive computational cost, and the algorithm works well on a wide range of different types of noise.

KW - Wavelet transform

KW - Complex diffusion process

KW - Image de-noising

KW - Thresholding

U2 - 10.1179/1743131X11Y.0000000024

DO - 10.1179/1743131X11Y.0000000024

JO - Imaging Science Journal

JF - Imaging Science Journal

SN - 1368-2199

IS - 4

VL - 60

SP - 208

EP - 218

ER -