UPM Institutional Repository

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


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


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.

Download File

Natural image.pdf

Download (5kB) | Preview

Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.1007/s00371-017-1439-9
Publisher: Springer Berlin Heidelberg
Keywords: Gaussian noise; Nonlocal means; Hidden Markov model; Spatial filter; Noise removal; Classification clustering
Depositing User: Mr. Sazali Mohamad
Date Deposited: 28 Nov 2019 05:39
Last Modified: 28 Nov 2019 05:39
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=/10.1007/s00371-017-1439-9
URI: http://psasir.upm.edu.my/id/eprint/75064
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item