NDLI: Image Denoising Based on Wavelet Domain Spatial Context Modeling

Content Provider	IEEE Xplore Digital Library
Author	Xuchao Li Shan'an Zhu
Copyright Year	2006
Description	Author affiliation: Coll. of Electr. Eng., Zhejiang Univ., Hangzhou (Xuchao Li)
Abstract	Using prior knowledge about the spatial clustering of the wavelet coefficients, a new image denoising method that applies the Bayesian framework is proposed. Wavelet coefficients of image are characterized by a two-state Gaussian mixture model (GMM), while their local spatial interactions are modeled by a Markov random field (MRF) model. The Expectation Maximization (EM) algorithm is used to estimate the parameters of the GMM, and an iterative updating technique known as iterative conditional modes (ICM) is applied to optimize the binary labels containing the positions of those wavelet coefficients that represent the useful signal in each subband. For each wavelet coefficient a shrinkage factor is finally determined, depending on its initial shrinkage factor and on the local spatial neighborhood in the label field. The qualitative and quantitative experimental results show that the new scheme outperforms other wavelet denosing methods, such as yielding significantly superior image quality, increasing peak signal-to-noise ratio (PSNR)
Starting Page	9504
Ending Page	9508
File Size	226369
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1713843
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image denoising Wavelet domain Context modeling Wavelet coefficients PSNR Bayesian methods Markov random fields Clustering algorithms Iterative algorithms Parameter estimation Wavelet coefficient Shrinkage factor Markov random field
Content Type	Text
Resource Type	Article

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