An image NSCT-HMT model based on copula entropy multivariate Gaussian scale mixtures

Wang, Xianghai; Song, Ruoxi; Mu, Zhenhua; Song, Chuanming

doi:10.1016/j.knosys.2019.105387

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…Jian et al [18] used the Hidden Markov Tree model (HMT) to capture the intra-dependency of NSST correlations, and the trained coefficients by HMT are selected based on local energy of gradients, which can greatly enhance the details in fused image. Wang et al [19] proposed a NSCT-HMT based on the Gaussian copula function to model the correlation between the current coefficient with its four neighbors. The application of copula entropy facilitating the localization of texture features in the source images, and provide good performance in image denoising.…”

Section: Related Workmentioning

confidence: 99%

Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model

Luo¹,

Jiang²,

Wang³

et al. 2022

Preprint

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The traditional two-state hidden Markov model divides the high frequency coefficients only into two states (large and small states). Such scheme is prone to produce an inaccurate sta-tistical model for the high frequency subband and reduces the quality of fusion result. In this paper, a fine-grained multi-state contextual hidden Markov model (MCHMM) is proposed for infrared and visible image fusion in the nonsubsampled Shearlet domain, which takes full consideration of the strong correlations and level of details of NSST coefficients. To this end, an accurate soft context variable is designed correspondingly from the perspective of context correlation. Then, the statistical features provided by MCHMM are utilized for the fusion of high frequency subbands. To ensure the visual quality, a fusion strategy based on the difference in regional energy is proposed as well for low-frequency subbands. Experimental results demonstrate that the proposed method can achieve a superior performance compared with other fusion methods in both subjective and objective aspects.

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Section: Related Workmentioning

confidence: 99%

Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model

Luo¹,

Jiang²,

Wang³

et al. 2022

Preprint

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“…After that, this concept is wieldy used in many different papers like [16,32,34]. In this paper, we are focusing on the mixture of copula function and entropy principle, and such articles as [3,12,18,31] are published in recent years. The idea is to make alike between the copula function and the maximum entropy to estimate the unclear copula and then approximating the indistinct distributions using the Sklar theorem [28].…”

Section: Copula Function Definitionmentioning

confidence: 99%

Dependence control chart using maximum copula entropy

Mortezanejad

Borzadaran

Gildeh

2020

Preprint

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Statistical quality control methods are noteworthy to produced standard production in manufacturing processes. In this regard, there are many classical manners to control the process. Many of them have a global assumption around distributions of the process data. They are supposed to be normal, which is clear that it is not always valid for all processes. Such control charts made some false decisions that waste funds. So, the main question while working with multivariate data set is how to find the multivariate distribution of the data set, which saves the original dependency between variables. Up to our knowledge, a copula function guarantees the dependence on the result function. But it is not enough when there is no other fundamental information about the statistical society, and we have just a data set. Therefore, we apply the maximum entropy concept to deal with this situation. In this paper, first of all, we find out the joint distribution of a data set, which is from a manufacturing process that needs to be control while running the production process. Then, we get an elliptical control limit via the maximum copula entropy. In the final step, we represent a practical example using the stated method. Average run lengths are calculated for some means and shifts to show the ability of the maximum copula entropy. In the end, two real data examples are presented.

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“…The benefits of the multiwww.ijacsa.thesai.org scale geometric transform-based methods are obvious since the features can be easy to capture in different scales, but they usually suffer from the disadvantages that the operations on the transformed coefficients may not match the distribution of the specific noise in different scales and directions. Though some typical models are proposed to alleviate the drawback, such as the generalized Gaussian distribution model in the wavelet and shearlet domain [21,22], the Hidden Markov Model in the wavelet, NSCT and shearlet domain [23][24][25], Gaussian scale mixture model [26], the results are still not satisfying.…”

Section: Introductionmentioning

confidence: 99%

The Medical Image Denoising Method Based on the CycleGAN and the Complex Shearlet Transform

Liu,

Huang,

Tahir

et al. 2023

IJACSA

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Medical image denoising plays an important role for the noise in the medical images can reduce the visibility, thereby affecting the diagnostic results of the doctors. Although good results have been achieved by the well-known deep learning-based denoising methods for their strong ability of learning, the loss of structural feature information and the well preservation of the edge information have not attracted considerable attention. To deal with these problems, a novel medical image denoising method based on the improved CycleGAN and the complex shearlet transform(CST) is proposed. The CST is used to construct the generator to embed more feature information in the training process and the denoising process is modeled to adversarial learn the mapping between the noise-free image domain and the noisy image domain. With the mechanism of the recurrent learning from the CycleGAN, the proposed method does not need the paired training data, which obviously speeds up the training and is more convenient than other classical methods. By comparing with five state-of-the-art denoising methods, experiments on the open dataset fully prove the accuracy and efficiency of the proposed method in terms of the visual quality and the quantitative PSNR, SSIM, and EPI.

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An image NSCT-HMT model based on copula entropy multivariate Gaussian scale mixtures

Cited by 7 publications

References 21 publications

Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model

Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model

Dependence control chart using maximum copula entropy

The Medical Image Denoising Method Based on the CycleGAN and the Complex Shearlet Transform

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