Statistical denoising scheme for single molecule fluorescence microscopic images
- Authors
- Yoon, Ji Won
- Issue Date
- Mar-2014
- Publisher
- ELSEVIER SCI LTD
- Keywords
- De-noising; Bayesian; Adaptive prior
- Citation
- BIOMEDICAL SIGNAL PROCESSING AND CONTROL, v.10, pp.11 - 20
- Indexed
- SCIE
SCOPUS
- Journal Title
- BIOMEDICAL SIGNAL PROCESSING AND CONTROL
- Volume
- 10
- Start Page
- 11
- End Page
- 20
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/99248
- DOI
- 10.1016/j.bspc.2013.12.005
- ISSN
- 1746-8094
- Abstract
- Single molecule fluorescence microscopy is a powerful technique for uncovering detailed information about biological systems, both in vitro and in vivo. In such experiments, the inherently low signal to noise ratios mean that accurate algorithms to separate true signal and background noise are essential to generate meaningful results. To this end, we have developed a new and robust method to reduce noise in single molecule fluorescence images by using a Gaussian Markov random field (GMRF) prior in a Bayesian framework Two different strategies are proposed to build the prior an intrinsic GMRF, with a stationary relationship between pixels and a heterogeneous intrinsic GMRF, with a differently weighted relationship between pixels classified as molecules and background. Testing with synthetic and real experimental fluorescence images demonstrates that the heterogeneous intrinsic GMRF is superior to other conventional de-noising approaches. (C) 2014 Elsevier Ltd. All rights reserved.
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