Robust Natural Image Denoising in Wavelet Domain using Hidden Markov Models

Khmag, Asem; Ramli, Abd Rahman; Al-Haddad, S. A. R.; Kamarudin, Noraziahtulhidayu; Aqel, Mohammad O. A.

doi:10.17485/ijst/2015/v8i32/92111

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…W ITH current image capturing technologies, digital images are inevitably contaminated by noise in the process of image acquisition and transmission. Image noise removal is mainly utilized in computer vision systems in order to increase the quality of the contaminated images because the natural digital images mostly suffer from additive white Gaussian noise (AWGN) [1]. Assume an ideal image x is contaminated by additive zero-mean white and homogeneous Gaussian noise θ, with standard deviation σ.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Collaborative Filtering Method for Image Noise and Artifact Removal

Liu

Wang

et al. 2020

IEEE Access

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In recent years, sparse representation theory and low-rank approximation model have been widely used in signal and image processing fields. In the study of natural image denoising, non-local similarity method can enhance the correlation of grouped image blocks, and a low rank prior is used to devise a bilateral sparse representation of the image matrix, consequently achieving the purpose of removing additive white Gaussian noise. However, problems such as how to select thresholds after a singular value shrinkage, and how to eliminate image artifacts in removing noise especially with high levels, have not been resolved. In this paper, a low rank adaptive singular value thresholding (ASVT) denoising algorithm based on singular value decomposition(SVD) is proposed. Our method uses random matrix and asymptotic matrix reconstruction theory to scientifically select the threshold of singular value thresholding. At the same time, a dual-domain filtering method is used to process the visual artifacts after image denoising by ASVT, which we call collaborative singular value thresholding (CSVT) algorithm. The experimental results show that the proposed algorithm has a certain improvement in subjective visual effects, and objective quantitative indicators compared with some related advanced denoising algorithms. INDEX TERMS Image denoising; Image enhancement; Low-rank approximation; Random matrix; Thresholding optimization; Dual-domain filtering.

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Section: Introductionmentioning

confidence: 99%

An Efficient Collaborative Filtering Method for Image Noise and Artifact Removal

Liu

Wang

et al. 2020

IEEE Access

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

et al. 2017

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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.

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

Khmag

Haddad

Ramlee

et al. 2017

Multimed Tools Appl

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Robust Natural Image Denoising in Wavelet Domain using Hidden Markov Models

Cited by 3 publications

References 19 publications

An Efficient Collaborative Filtering Method for Image Noise and Artifact Removal

An Efficient Collaborative Filtering Method for Image Noise and Artifact Removal

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

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

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