A variational model based on split Bregman method for multiplicative noise removal

Hu, Yan; Xu, Jianlou; Li, Shuaijie; Zhang, Xuande

doi:10.1016/j.aeue.2015.05.009

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…The PSNR and error cures are shown in Figure 4. From Figure 4, we can see that the two methods can obtain the almost same PSNR with the iteration times increasing; however the new method is faster than [15] to achieve the best PSNR. In addition, the new method is strictly convex and the convergence proof is given, but [15] does not give the convergence proof.…”

Section: Resultsmentioning

confidence: 93%

“…and 3(f), we can see that the proposed algorithm can obtain the better results when ̸ = 0. Finally, we take House image as an example to compare the new method with the recent model [15] for noise level = 9. Reference [15] adopts the relaxed alternation direction method and primal-dual method.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed model has the following advantages. Firstly, the proposed model is strictly convex and it has a unique minimal solution, which is different from the models [4,12,15]. Secondly, when tends to be infinite, k tends to ∇ , and the first two terms turn into the second-order TV, which has been proposed by the authors in [23].…”

Section: The Proposed Model and Algorithmmentioning

confidence: 95%

“…Using the constrained TV norm, Hao et al [13] put forward a dual method and its acceleration. For other methods for multiplicative noise removal refer to [14][15][16][17][18][19][20][21][22]. However, it is well known that the TV model suffers from the so-called staircase effect.…”

Section: Introductionmentioning

confidence: 99%

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Multiplicative Noise Removal Based on the Linear Alternating Direction Method for a Hybrid Variational Model

Feng-yun

et al. 2017

Mathematical Problems in Engineering

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To preserve the edge, multiplicative noise removal models based on the total variation regularization have been widely studied, but they suffer from the staircase effect. In this paper, to preserve the edge and reduce the staircase effect, we develop a hybrid variational model based on the variable splitting method for multiplicative noise removal; the new model is a strictly convex objective function which contains the total variation regularization and a modified regularization term. We use the linear alternative direction method to find the minimal solution and also give the convergence proof of the proposed algorithm. Experimental results verify that the proposed model can obtain the better results for removing the multiplicative noise compared with the recent method.

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Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: The Proposed Model and Algorithmmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Multiplicative Noise Removal Based on the Linear Alternating Direction Method for a Hybrid Variational Model

Feng-yun

et al. 2017

Mathematical Problems in Engineering

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“…In addition, the regularization parameters are computed adaptively for each band and derived from the estimated noise standard deviation using the coefficient of the highest frequency wavelet subband. The obtained minimization problem is solved using Bregmanized operator splitting [41,42,43] which introduces an auxiliary variable. The unconstrained problem is treated using Bregman iteration method which leads to an update algorithm where Gauss-Seidel and shrinkage methods are used.…”

Section: Related Workmentioning

confidence: 99%

Multispectral image denoising with optimized vector non-local mean filter

Said

Hadjidj

Melkemi

et al. 2016

Digital Signal Processing

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Nowadays, many applications rely on images of high quality to ensure good performance in conducting their tasks. However, noise goes against this objective as it is an unavoidable issue in most applications. Therefore, it is essential to develop techniques to attenuate the impact of noise, while maintaining the integrity of relevant information in images. We propose in this work to extend the application of the Non-Local Means filter (NLM) to the vector case and apply it for denoising multispectral images. The objective is to benefit from the additional information brought by multispectral imaging systems. The NLM filter exploits the redundancy of information in an image to remove noise. A restored pixel is a weighted average of all pixels in the image. In our contribution, we propose an optimization framework where we dynamically fine tune the NLM filter parameters and attenuate its computational complexity by considering only pixels which are most similar to each other in computing a restored pixel. Filter parameters are optimized using Stein's Unbiased Risk Estimator (SURE) rather than using ad hoc means. Experiments have been conducted on multispectral images corrupted with additive white Gaussian noise and PSNR and similarity comparison with other approaches are provided to illustrate the efficiency of our approach in terms of both denoising performance and computation complexity.

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A Nonlinear Coupled Diffusion System for Image Despeckling and Application to Ultrasound Images

Jain

Ray

Bhavsar

2018

Circuits Syst Signal Process

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A variational model based on split Bregman method for multiplicative noise removal

Cited by 17 publications

References 28 publications

Multiplicative Noise Removal Based on the Linear Alternating Direction Method for a Hybrid Variational Model

Multiplicative Noise Removal Based on the Linear Alternating Direction Method for a Hybrid Variational Model

Multispectral image denoising with optimized vector non-local mean filter

A Nonlinear Coupled Diffusion System for Image Despeckling and Application to Ultrasound Images

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