scholarly journals Poisson-Gaussian Noise Reduction Using the Hidden Markov Model in Contourlet Domain for Fluorescence Microscopy Images

PLoS ONE ◽  
2015 ◽  
Vol 10 (9) ◽  
pp. e0136964 ◽  
Author(s):  
Sejung Yang ◽  
Byung-Uk Lee
Keyword(s):  
Fluorescence Microscopy ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Noise Reduction ◽  
Gaussian Noise ◽  
Hidden Markov ◽  
Microscopy Images

IMAGE RESTORATION: THE WAVELET-BASED APPROACH

2003 ◽  
Vol 17 (01) ◽  
pp. 151-162 ◽  
Author(s):  
TERTULIEN NDJOUNTCHE ◽  
ROLF UNBEHAUEN
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Noise Reduction ◽  
Performance Improvement ◽  
Hidden Markov ◽  
Reduction Technique ◽  
Statistical Characteristics ◽  
Signal Distortion ◽  
Significant Performance ◽  
Noise Reduction Technique

Wavelet-based techniques are suitable for recovering a signal corrupted by noise. The time- and frequency-localization capabilities of wavelets provide better noise reduction and less signal distortion than conventional filtering methods. The noise reduction technique used in this paper is based on the hidden Markov model (HMM) structure, which can efficiently shape the statistical characteristics of practical data. As confirmed by numerical results, the HMM based approach provides a significant performance improvement over competing methods.

EM algorithm for Markov chains observed via Gaussian noise and point process information: Theory and case studies

2018 ◽  
Vol 35 (1-2) ◽  
pp. 51-72 ◽  
Author(s):  
Camilla Damian ◽  
Zehra Eksi ◽  
Rüdiger Frey
Keyword(s):  
Markov Model ◽  
Em Algorithm ◽  
Credit Risk ◽  
Hidden Markov Model ◽  
Point Process ◽  
Gaussian Noise ◽  
Goodness Of Fit ◽  
Markov Models ◽  
Hidden Markov ◽  
Process Observation

AbstractIn this paper we study parameter estimation via the Expectation Maximization (EM) algorithm for a continuous-time hidden Markov model with diffusion and point process observation. Inference problems of this type arise for instance in credit risk modelling. A key step in the application of the EM algorithm is the derivation of finite-dimensional filters for the quantities that are needed in the E-Step of the algorithm. In this context we obtain exact, unnormalized and robust filters, and we discuss their numerical implementation. Moreover, we propose several goodness-of-fit tests for hidden Markov models with Gaussian noise and point process observation. We run an extensive simulation study to test speed and accuracy of our methodology. The paper closes with an application to credit risk: we estimate the parameters of a hidden Markov model for credit quality where the observations consist of rating transitions and credit spreads for US corporations.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions
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