NDLI: Locally adaptive speckle noise reduction using maximum a posteriori estimation based on Maxwell distribution

Content Provider	IEEE Xplore Digital Library
Author	Sung Gug Kim Yoo Shin Kim Il Kyu Eom
Copyright Year	2009
Description	Author affiliation: School of Electrical Engineering, Pusan National University, Busan, South Korea (Sung Gug Kim; Yoo Shin Kim; Il Kyu Eom)
Abstract	This paper introduces a speckle noise reduction algorithm using Bayesian estimation in the wavelet domain. The wavelet coefficients of the log-transformed signal are modeled by Laplacian distribution, while those of the log-transformed speckle are modeled by Maxwell distribution. The Bayesian maximum a posteriori (MAP) estimation is basically based on the presumption that speckle is spatially correlated within a small window. In this paper, the window size is automatically regulated depending on the statistics, such as mean and variance. Simulations are performed using synthetically real speckled ultrasound (US)) image and peppers image. The results show that the proposed method can conduct better than some of the existing methods in terms of the Peak Signal to Noise Ratio (PSNR) and the edge preservation factor.
Starting Page	157
Ending Page	160
File Size	886830
Page Count	4
File Format	PDF
ISBN	9781424443352
ISSN	15206130
DOI	10.1109/SIPS.2009.5336242
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-07
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speckle Noise reduction Maximum a posteriori estimation Maxwell-Boltzmann distribution Bayesian methods PSNR Wavelet domain Wavelet coefficients Laplace equations Ultrasonic imaging Maxwell distribution Speckle noise wavelet transform Denoising MAP ultrasound image
Content Type	Text
Resource Type	Article

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