NDLI: Markov random field modeling in the wavelet domain for image denoising

Content Provider	IEEE Xplore Digital Library
Author	Yan-Qiu Cui Ke Wang
Copyright Year	2005
Description	Author affiliation: Coll. of Commun. Eng., Jilin Univ., Changchun, China (Yan-Qiu Cui; Ke Wang)
Abstract	This paper presents an effective Bayesian wavelet shrinkage approach, in which a new Markov random field (MRF) model is used to improve the noise suppression performance. It is known that relatively few large wavelet coefficients carry the essential information of an image. These coefficients tend to cluster around the location of important feature in the image, such as edge discontinuities, peaks, and corners. This spatial clustering property is expressed in a prior model in the Bayesian framework. This paper proposes a novel prior model that models the configurations of the wavelet coefficients as a MRF. This model is adaptive to the wavelet transform character of two-dimension images that significant coefficients form clusters with predominantly the horizontal, vertical, or diagonal direction in each orientation subband. This prior model consequently effects the manipulation of the coefficients and a spatially adaptive Bayesian shrinkage function is obtained. Experimental results demonstrate this method can effectively suppress additive Gaussian white noise and preserve the details of the image. The performance surpasses related earlier methods quantitatively and qualitatively.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	5382
Ending Page	5387
File Size	606935
Page Count	6
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527895
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov random fields Wavelet domain Image denoising Wavelet coefficients Bayesian methods Wavelet transforms Noise reduction Collaboration Anisotropic magnetoresistance Educational institutions wavelet Markov random field
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Image Denoising Based on Wavelet Domain Spatial Context Modeling

A complex wavelet domain Markov model for image denoising

Research on adaptive image denoising based on wavelet transform

A wavelet based image denoising using statistical sampler for Bayesian estimator

Context-based bias removal of statistical models of wavelet coefficients for image denoising

A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising

Image Denoising Based on Fuzzy in Wavelet Domain

Image denoising based on a statistical model for wavelet coefficients

Wavelet-Domain Image Denoising Using Contextual Hidden Markov Tree Model

Markov random field modeling in the wavelet domain for image denoising

Similar Documents

Image Denoising Based on Wavelet Domain Spatial Context Modeling

A complex wavelet domain Markov model for image denoising

Research on adaptive image denoising based on wavelet transform

A wavelet based image denoising using statistical sampler for Bayesian estimator

Context-based bias removal of statistical models of wavelet coefficients for image denoising

A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising

Image Denoising Based on Fuzzy in Wavelet Domain

Image denoising based on a statistical model for wavelet coefficients

Wavelet-Domain Image Denoising Using Contextual Hidden Markov Tree Model

Markov random field modeling in the wavelet domain for image denoising