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Poisson-Gaussian noise reduction using the hidden Markov model in contourlet domain for fluorescence microscopy images

Title
Poisson-Gaussian noise reduction using the hidden Markov model in contourlet domain for fluorescence microscopy images
Authors
YangS.LeeB.-U.
Ewha Authors
이병욱양세정
SCOPUS Author ID
이병욱scopus; 양세정scopus
Issue Date
2015
Journal Title
PLoS ONE
ISSN
1932-6203JCR Link
Citation
vol. 10, no. 9
Publisher
Public Library of Science
Indexed
SCIE; SCOPUS WOS scopus
Abstract
In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach. Copyright: © 2015 Yang, Lee.
DOI
10.1371/journal.pone.0136964
Appears in Collections:
엘텍공과대학 > 전자공학과 > Journal papers
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