NDLI: A nonlocal means based adaptive denoising framework for mixed image noise removal

Content Provider	IEEE Xplore Digital Library
Author	Zhu Lin
Copyright Year	2013
Abstract	Most existing image denoising algorithms can only deal with a single type of noise; however, real world images are typically contaminated by more than one type of noise during image acquisition and transmission process. Recently, nonlocal approaches got great success in removing Gaussian noise; however, they cannot deal with impulse noise due to their nature. In this paper, we propose an improved nonlocal means (NL-means) to simultaneously remove impulse noise and Gaussian noise. An adaptive mixed noise removal framework based on the improved NL-means is also introduced. Comparing with existing NL-means based mixed noise removal frameworks which remove one type of noise at a time; the proposed framework can remove mixed noise simultaneously in a single step. Experimental results show that the proposed denoising framework has better denoising performance for mixed noise; meanwhile it is much more efficient which makes future parallel optimization such as GPU optimization possible.
Starting Page	454
Ending Page	458
File Size	500401
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738094
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise reduction Gaussian noise Noise measurement Image denoising PSNR Detectors Gaussian-impulse noise mixed noise removal nonlocal means
Content Type	Text
Resource Type	Article

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