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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer Berlin Heidelberg
2018
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/75064/1/Natural%20image.pdf |
| _version_ | 1825950406267633664 |
|---|---|
| author | Asem, Khmag Sy Mohamed, Syed Abdul Rahman Al Haddad Ramlee, Ridza Azri Kamarudin, Noraziahtulhidayu Malallah, Fahad Layth |
| author_facet | Asem, Khmag Sy Mohamed, Syed Abdul Rahman Al Haddad Ramlee, Ridza Azri Kamarudin, Noraziahtulhidayu Malallah, Fahad Layth |
| author_sort | Asem, Khmag |
| collection | UPM |
| 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. |
| first_indexed | 2024-03-06T10:14:06Z |
| format | Article |
| id | upm.eprints-75064 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:14:06Z |
| publishDate | 2018 |
| publisher | Springer Berlin Heidelberg |
| record_format | dspace |
| spelling | 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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/75064/1/Natural%20image.pdf |
| work_keys_str_mv | AT asemkhmag naturalimagenoiseremovalusingnonlocalmeansandhiddenmarkovmodelsintransformdomain AT symohamedsyedabdulrahmanalhaddad naturalimagenoiseremovalusingnonlocalmeansandhiddenmarkovmodelsintransformdomain AT ramleeridzaazri naturalimagenoiseremovalusingnonlocalmeansandhiddenmarkovmodelsintransformdomain AT kamarudinnoraziahtulhidayu naturalimagenoiseremovalusingnonlocalmeansandhiddenmarkovmodelsintransformdomain AT malallahfahadlayth naturalimagenoiseremovalusingnonlocalmeansandhiddenmarkovmodelsintransformdomain |