Natural image noise removal using non local means and hidden Markov models in stationary wavelet transform domain

作者：Khmag Asem^*; Al Haddad Syed Abdul Rahman; Ramlee Ridza Azri; Kamarudin Noraziahtulhidayu; Malallah Fahad Layth

来源：Multimedia Tools and Applications, 2018, 77(15): 20065-20086.

DOI：10.1007/s11042-017-5425-z

• Additive noise
• Non-local means
• Hidden Markov model
• Spatial filter
• Image denoising
• Multiscale clustering

In self-similarity digital image features, nonlocal means (NLM) exploits the major aspects when it comes to noise removal methods. Despite the high performance characteristics that NLM has proven, computational complexity yet to be highly achieved especially in case of complicated texture patches. In this regard, this study uses the clustered batches of noisy images and hidden Markov models (HMMs) in order to achieve noiseless images where the dependency between additive noise model pixels and its neighbors on stationary wavelet transform is found using HMMs. This paper is helpful and significant in order to develop a speedy and efficient plant recognition system computer-based to identify the plant species. The pivotal significant of the use of NLM and HMMs in this study is to ensure the statistical properties of the wavelet transform such as multiscale dependency among the wavelet coefficients, local correlation in neighbourhood coefficients. Practically, the experimental results present that the proposed algorithm has depicts high visual quality images in the experiments that are conducted in this study, apart from the objective analysis of the proposed algorithm, the execution time and its complexity show a competitive performance with state of the art noise removal methods in low and high noise levels.

• 出版日期2018-8