Image denoising using Contourlet Transform with variable block thresholding

Tariq M. Salman; Maher K. Mahmood Al-Azawi; Laith Al-Jobouri

doi:10.1109/CEEC.2018.8674209

Image denoising using Contourlet Transform with variable block thresholding

Tariq M. Salman, Maher K. Mahmood Al-Azawi, Laith Al-Jobouri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Denoising is one of the main issues in digital image processing systems. This paper presents a new idea to improve the Peak Signal to Noise Ratio (PSNR) of the denoised image by dividing the image into many blocks then a variable threshold is set according to the energy content of a particular block in the frequency domain obtained using the Contourlet Transform (CT). Computer simulation results show that the proposed method is very efficient to denoise Magnetic Resonance Imaging (MRI) pictures and outperforms the traditional denoising methods that deal with the image as a whole. Experimental evaluation showed that the variable block threshold is more efficient when applied at MRI than other type of images, due to the low energy of the surrounding areas in the MRI.

Original language	English
Title of host publication	2018 10th Computer Science and Electronic Engineering Conference (CEEC)
Subtitle of host publication	conference proceedings
Place of Publication	Piscataway
Publisher	IEEE
Pages	282-286
Number of pages	5
ISBN (Electronic)	9781538672754, 9781538672747
ISBN (Print)	9781538672761
DOIs	https://doi.org/10.1109/CEEC.2018.8674209
Publication status	Published - 28 Mar 2019
Externally published	Yes
Event	10th Computer Science and Electronic Engineering Conference - University of Essex, Colchester, United Kingdom Duration: 19 Sept 2018 → 21 Sept 2018 Conference number: 10th

Conference

Conference	10th Computer Science and Electronic Engineering Conference
Abbreviated title	CEEC 2018
Country/Territory	United Kingdom
City	Colchester
Period	19/09/18 → 21/09/18

Keywords

Blocking
Block thresholding
Contourlet
MRI images and Standard Deviation of Blocks

Access to Document

10.1109/CEEC.2018.8674209

Cite this

@inproceedings{c14a4a82a0954bd99cb21d70581e72cf,

title = "Image denoising using Contourlet Transform with variable block thresholding",

abstract = "Denoising is one of the main issues in digital image processing systems. This paper presents a new idea to improve the Peak Signal to Noise Ratio (PSNR) of the denoised image by dividing the image into many blocks then a variable threshold is set according to the energy content of a particular block in the frequency domain obtained using the Contourlet Transform (CT). Computer simulation results show that the proposed method is very efficient to denoise Magnetic Resonance Imaging (MRI) pictures and outperforms the traditional denoising methods that deal with the image as a whole. Experimental evaluation showed that the variable block threshold is more efficient when applied at MRI than other type of images, due to the low energy of the surrounding areas in the MRI.",

author = "Salman, \{Tariq M.\} and Al-Azawi, \{Maher K. Mahmood\} and Laith Al-Jobouri",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 10th Computer Science and Electronic Engineering Conference, CEEC 2018 ; Conference date: 19-09-2018 Through 21-09-2018",

year = "2019",

month = mar,

day = "28",

doi = "10.1109/CEEC.2018.8674209",

language = "English",

isbn = "9781538672761",

pages = "282--286",

booktitle = "2018 10th Computer Science and Electronic Engineering Conference (CEEC)",

publisher = "IEEE ",

}

Salman, TM, Al-Azawi, MKM & Al-Jobouri, L 2019, Image denoising using Contourlet Transform with variable block thresholding. in 2018 10th Computer Science and Electronic Engineering Conference (CEEC): conference proceedings. IEEE , Piscataway, pp. 282-286, 10th Computer Science and Electronic Engineering Conference, Colchester, United Kingdom, 19/09/18. https://doi.org/10.1109/CEEC.2018.8674209

TY - GEN

T1 - Image denoising using Contourlet Transform with variable block thresholding

AU - Salman, Tariq M.

AU - Al-Azawi, Maher K. Mahmood

AU - Al-Jobouri, Laith

N1 - Conference code: 10th

PY - 2019/3/28

Y1 - 2019/3/28

N2 - Denoising is one of the main issues in digital image processing systems. This paper presents a new idea to improve the Peak Signal to Noise Ratio (PSNR) of the denoised image by dividing the image into many blocks then a variable threshold is set according to the energy content of a particular block in the frequency domain obtained using the Contourlet Transform (CT). Computer simulation results show that the proposed method is very efficient to denoise Magnetic Resonance Imaging (MRI) pictures and outperforms the traditional denoising methods that deal with the image as a whole. Experimental evaluation showed that the variable block threshold is more efficient when applied at MRI than other type of images, due to the low energy of the surrounding areas in the MRI.

AB - Denoising is one of the main issues in digital image processing systems. This paper presents a new idea to improve the Peak Signal to Noise Ratio (PSNR) of the denoised image by dividing the image into many blocks then a variable threshold is set according to the energy content of a particular block in the frequency domain obtained using the Contourlet Transform (CT). Computer simulation results show that the proposed method is very efficient to denoise Magnetic Resonance Imaging (MRI) pictures and outperforms the traditional denoising methods that deal with the image as a whole. Experimental evaluation showed that the variable block threshold is more efficient when applied at MRI than other type of images, due to the low energy of the surrounding areas in the MRI.

U2 - 10.1109/CEEC.2018.8674209

DO - 10.1109/CEEC.2018.8674209

M3 - Conference contribution

AN - SCOPUS:85064387736

SN - 9781538672761

SP - 282

EP - 286

BT - 2018 10th Computer Science and Electronic Engineering Conference (CEEC)

PB - IEEE

CY - Piscataway

T2 - 10th Computer Science and Electronic Engineering Conference

Y2 - 19 September 2018 through 21 September 2018

ER -

Image denoising using Contourlet Transform with variable block thresholding

Abstract

Conference

Keywords

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