NDLI: Block Adaptive Bayesian Wavelet Shrinkage for 2D Signal De-noising

Content Provider	IEEE Xplore Digital Library
Author	Zhang, Dachun Liu, Gang Li, Hongbin Chu, Deqiang Kang, Yuebin
Copyright Year	2008
Abstract	A block adaptive Bayesian wavelet shrinkage is proposed in this paper to accommodate the reduction of a kind of two-dimensionally, signal amplitude-related contamination, which is taken place in some military and biomedical applications. Wavelet shrinkage conventionally works under the assumption of signal-independent, additive Gaussian noise. For the temporally Gaussian but spatially signal amplitude-related noise, its denoising efficiency is depressed. To make use of the merit of wavelet denoising, the signal space is split to blocks and then wavelet shrinkage is conducted in each block, in which the signal amplitude is assumed to vary little. Results from simulation show that the proposed method outperforms the traditional one in signal-to-noise ratio improvement.
Starting Page	302
Ending Page	306
File Size	421531
Page Count	5
File Format	PDF
ISBN	9780769531199
DOI	10.1109/CISP.2008.69
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Additive noise Line wavelet denoising (LWD) Noise reduction Adaptive signal processing Bayesian wavelet shrinkage Signal denoising Block wavelet denoising (BWD) Contamination Noise level Wavelet coefficients Bayesian methods Gaussian noise Biomedical signal processing
Content Type	Text
Resource Type	Article

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