NDLI: Robust Impulse Noise Variance Estimation Based on Image Histogram

Content Provider	IEEE Xplore Digital Library
Author	Yi Wan Qiqiang Chen Yan Yang
Copyright Year	1994
Abstract	The state of the art impulse noise removal methods make use of the noise variance, or equivalently the noise mixing probability p, and are iterative procedures (e.g., , ). However, so far there has been a lack of effective estimator for p. As a result, true values of p are often used during simulation, which may not be practical. Furthermore, the optimal stopping criteria for the iterative algorithms have been elusive until recently. In a computationally heavy method is proposed for determining the optimal number of iterations. In this letter we make two contributions. We first develop a robust estimator for p by using the empirical observation that a natural image usually doesn't cover all pixel value range, then we design an efficient linear transformation to replace complicated computation of order statistics. Based on this estimated p value, we further derive the formula for estimating the true image histogram, and use it to formulate a new efficient optimal stopping criterion during the iterative denoising process. This formulation has a simple interpretation of its optimality and yields improved denoising performance.
Sponsorship	IEEE Signal Processing Society
Starting Page	485
Ending Page	488
Page Count	4
File Size	1032296
File Format	PDF
ISSN	10709908
Volume Number	17
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise robustness Histograms Noise reduction Additive noise Pixel Gaussian noise Noise level Iterative methods Iterative algorithms Additive white noise robust estimation Histogram image denoising impulse noise noise variance optimal iteration number
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing Electrical and Electronic Engineering

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