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Natural image noise removal using nonlocal means and hidden Markov models in transform domain

Nonlocal means (NLM) which utilizes the self-similarity is considered as one of the most popular denoising techniques. Although NLM can attain significant performance, it shows a few loopholes, such as its computational complexity when it comes to similarity measurements, and the small number of suf...

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Main Authors: Asem, Khmag, Sy Mohamed, Syed Abdul Rahman Al Haddad, Ramlee, Ridza Azri, Kamarudin, Noraziahtulhidayu, Malallah, Fahad Layth
Format: Article
Language:English
Published: Springer Berlin Heidelberg 2018
Online Access:http://psasir.upm.edu.my/id/eprint/75064/1/Natural%20image.pdf
http://psasir.upm.edu.my/id/eprint/75064/
https://link.springer.com/article/10.1007/s00371-017-1439-9
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spelling my.upm.eprints.750642019-11-28T05:39:54Z http://psasir.upm.edu.my/id/eprint/75064/ Natural image noise removal using nonlocal means and hidden Markov models in transform domain Asem, Khmag Sy Mohamed, Syed Abdul Rahman Al Haddad Ramlee, Ridza Azri Kamarudin, Noraziahtulhidayu Malallah, Fahad Layth Nonlocal means (NLM) which utilizes the self-similarity is considered as one of the most popular denoising techniques. Although NLM can attain significant performance, it shows a few loopholes, such as its computational complexity when it comes to similarity measurements, and the small number of sufficient candidates that use to choose the target patches which have complicated textures. In this paper, the use of clustering based on moment invariants and the hidden Markov model (HMM) is proposed to achieve preclassification and thus capture the dependency between additive white Gaussian noise pixel and its neighbors on the wavelet transform. The HMM also allows hidden states to connect to one another to capture the dependencies among coefficients in the transform domain. In the practical part, the experimental results present that the proposed algorithm has the ability to show denoised images better than the results of state-of-the-art denoising methods both objectively in peak signal-to-noise ratio and structural similarity and subjectively using visual results, especially when the noise level is high. Springer Berlin Heidelberg 2018-12 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/75064/1/Natural%20image.pdf Asem, Khmag and Sy Mohamed, Syed Abdul Rahman Al Haddad and Ramlee, Ridza Azri and Kamarudin, Noraziahtulhidayu and Malallah, Fahad Layth (2018) Natural image noise removal using nonlocal means and hidden Markov models in transform domain. The Visual Computer, 34 (12). 1661 - 1675. ISSN 0178-2789; ESSN: 1432-2315 https://link.springer.com/article/10.1007/s00371-017-1439-9 10.1007/s00371-017-1439-9
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Nonlocal means (NLM) which utilizes the self-similarity is considered as one of the most popular denoising techniques. Although NLM can attain significant performance, it shows a few loopholes, such as its computational complexity when it comes to similarity measurements, and the small number of sufficient candidates that use to choose the target patches which have complicated textures. In this paper, the use of clustering based on moment invariants and the hidden Markov model (HMM) is proposed to achieve preclassification and thus capture the dependency between additive white Gaussian noise pixel and its neighbors on the wavelet transform. The HMM also allows hidden states to connect to one another to capture the dependencies among coefficients in the transform domain. In the practical part, the experimental results present that the proposed algorithm has the ability to show denoised images better than the results of state-of-the-art denoising methods both objectively in peak signal-to-noise ratio and structural similarity and subjectively using visual results, especially when the noise level is high.
format Article
author Asem, Khmag
Sy Mohamed, Syed Abdul Rahman Al Haddad
Ramlee, Ridza Azri
Kamarudin, Noraziahtulhidayu
Malallah, Fahad Layth
spellingShingle Asem, Khmag
Sy Mohamed, Syed Abdul Rahman Al Haddad
Ramlee, Ridza Azri
Kamarudin, Noraziahtulhidayu
Malallah, Fahad Layth
Natural image noise removal using nonlocal means and hidden Markov models in transform domain
author_facet Asem, Khmag
Sy Mohamed, Syed Abdul Rahman Al Haddad
Ramlee, Ridza Azri
Kamarudin, Noraziahtulhidayu
Malallah, Fahad Layth
author_sort Asem, Khmag
title Natural image noise removal using nonlocal means and hidden Markov models in transform domain
title_short Natural image noise removal using nonlocal means and hidden Markov models in transform domain
title_full Natural image noise removal using nonlocal means and hidden Markov models in transform domain
title_fullStr Natural image noise removal using nonlocal means and hidden Markov models in transform domain
title_full_unstemmed Natural image noise removal using nonlocal means and hidden Markov models in transform domain
title_sort natural image noise removal using nonlocal means and hidden markov models in transform domain
publisher Springer Berlin Heidelberg
publishDate 2018
url http://psasir.upm.edu.my/id/eprint/75064/1/Natural%20image.pdf
http://psasir.upm.edu.my/id/eprint/75064/
https://link.springer.com/article/10.1007/s00371-017-1439-9
_version_ 1651869170935005184
score 13.211869

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