Image denoising in hybrid wavelet and quincunx diamond filter bank domain based on Gaussian scale mixture model

Shanthi, S.; Sulochana, C. Helen; Latha, T.

doi:10.1016/j.compeleceng.2015.02.002

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…Based on the Gaussian scale mixture model, the image denoising method that was proposed in Ref. 28 and called wavelet and Gaussian scale mixture (WGSM).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…(1) Based on the Gaussian scale mixture model, the image denoising method that was proposed in Ref. 28 and called wavelet and Gaussian scale mixture (WGSM). ( 2) The method based on an adaptive threshold and optimized weighted median filter that was proposed in Ref.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Unlike Chantas et al., 27 who utilized the Student-t distribution as the prior term and solved the variational Bayesian model by approximation of a lower bound on the logarithm of the marginal likelihood, or Shanthi et al., 28 who utilized the Gaussian scale mixture distribution as the prior term and converted their model into L2-norm, we explicitly utilize the general Laplacian distribution as the prior term and convert the sparse model into L1-norm. In fact, compared with L2-norm, L1-norm is more specifically suitable for image sparsity.…”

Section: Variational Bayesian Model For Denoisingmentioning

confidence: 99%

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Ultrasonic logging image denoising algorithm based on variational Bayesian and sparse prior

2023

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An image denoising method is proposed for ultrasonic logging images with severe noise. The proposed method works on a variational Bayesian framework using block sparse prior. First, the sparse coefficients are simulated by a more appropriate distribution-Laplacian distribution. Then the variational Bayesian denoising model in which Laplacian distribution is used as a prior term of sparse coefficients is proposed. Finally, semiquadratic regularization is used to solve the model with a simplified process. Moreover, during the denoising process, a relaxation factor is introduced to improve the accuracy. In the vast majority of cases, the proposed method obtained better results in both the visual quality and the objective evaluation. It achieves better denoising performance than the existing denoising methods when the edge details of the images are contaminated by noise, especially severe noise. The experimental results show that the proposed method is practical in ultrasonic logging images.

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“…Based on the Gaussian scale mixture model, the image denoising method that was proposed in Ref. 28 and called wavelet and Gaussian scale mixture (WGSM).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Variational Bayesian Model For Denoisingmentioning

confidence: 99%

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Ultrasonic logging image denoising algorithm based on variational Bayesian and sparse prior

2023

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“…Gaussian filter (GS) [2] is a conventional low-pass filter, which is commonly combined with other methods to handle high-level noise or coarse texture. For example, the Gaussian mixture model [19][20][21] integrate two or more Gaussian filters, whose parameters are obtained by learning-based methods. The combination strategy is also applied for image restoration and reconstruction [22].…”

Section: Related Workmentioning

confidence: 99%

Window‐aware guided image filtering via local entropy

Liu

Yang

Wang

2021

IET Image Processing

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Guided image filtering is one of the widely used techniques in computer vision. However, it commonly leads to over‐smoothed edges and a distorted appearance when tackling intricate texture patterns and complex noise. In this paper, a window‐aware image filtering framework based on the bilateral filter guided by the local entropy is presented. The key idea of the authors' proposed approach is to design a novel guidance input and a non‐box filtering window. Specifically, using the Gaussian spatial kernel and the local entropy, a GEF that can maintain image feature details and yield a robust guidance input for BF is constructed. Meanwhile, based on an intensity‐similar strategy, the local non‐box filtering window is designed for the further preservation of edge structures. The authors' approach not only inherits the advantages of bilateral filter i.e. simplicity, parallelisation and easiness of programming, but also is more powerful than bilateral filter and its variants. In addition, the guided entropy filter and the non‐box window can also be transplanted to other local filters and can effectively improve the filtering effects. The qualitative and quantitative experimental results demonstrate that the authors' approach has good performance in image denoising, texture (or background) smoothing, edge extraction and other applications in image processing.

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Reduction AWGN from Digital Images Using a New Local Optimal Low-Rank Approximation Method

Kalantari

Ramezani

Madadi

et al. 2020

Proceedings of the 5th Brazilian Technology Symposium

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Image denoising in hybrid wavelet and quincunx diamond filter bank domain based on Gaussian scale mixture model

Cited by 8 publications

References 34 publications

Ultrasonic logging image denoising algorithm based on variational Bayesian and sparse prior

Ultrasonic logging image denoising algorithm based on variational Bayesian and sparse prior

Window‐aware guided image filtering via local entropy

Reduction AWGN from Digital Images Using a New Local Optimal Low-Rank Approximation Method

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