IMAGE RESTORATION: THE WAVELET-BASED APPROACH

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.

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

PLoS ONE ◽

10.1371/journal.pone.0136964 ◽

2015 ◽

Vol 10 (9) ◽

pp. e0136964 ◽

Cited By ~ 12

Author(s):

Sejung Yang ◽

Byung-Uk Lee

Keyword(s):

Fluorescence Microscopy ◽

Markov Model ◽

Hidden Markov Model ◽

Noise Reduction ◽

Gaussian Noise ◽

Hidden Markov ◽

Microscopy Images

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Investigation of Feature Elements and Performance Improvement for Sign Language Recognition by Hidden Markov Model

Universal Access in Human–Computer Interaction. Designing Novel Interactions - Lecture Notes in Computer Science ◽

10.1007/978-3-319-58703-5_6 ◽

2017 ◽

pp. 76-88 ◽

Cited By ~ 2

Author(s):

Tatsunori Ozawa ◽

Hirotoshi Shibata ◽

Hiromitsu Nishimura ◽

Hiroshi Tanaka

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Sign Language ◽

Performance Improvement ◽

Hidden Markov ◽

Language Recognition ◽

Sign Language Recognition ◽

And Performance

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Mobile Application Identification based on Hidden Markov Model

ITM Web of Conferences ◽

10.1051/itmconf/20181702002 ◽

2018 ◽

Vol 17 ◽

pp. 02002

Author(s):

Xinyan Yang ◽

Yunhui Yi ◽

Xinguang Xiao ◽

Yanhong Meng

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Network Management ◽

Mobile Application ◽

Network Flows ◽

Hidden Markov ◽

Mobile Network ◽

Mobile Internet ◽

Statistical Characteristics ◽

Application Identification

With the increasing number of mobile applications, there has more challenging network management tasks to resolve. Users also face security issues of the mobile Internet application when enjoying the mobile network resources. Identifying applications that correspond to network traffic can help network operators effectively perform network management. The existing mobile application recognition technology presents new challenges in extensibility and applications with encryption protocols. For the existing mobile application recognition technology, there are two problems, they can not recognize the application which using the encryption protocol and their scalability is poor. In this paper, a mobile application identification method based on Hidden Markov Model(HMM) is proposed to extract the defined statistical characteristics from different network flows generated when each application starting. According to the time information of different network flows to get the corresponding time series, and then for each application to be identified separately to establish the corresponding HMM model. Then, we use 10 common applications to test the method proposed in this paper. The test results show that the mobile application recognition method proposed in this paper has a high accuracy and good generalization ability.

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