An algorithm for image restoration with mixed noise using total variation regularization

Pham, Cong Thang; Gamard, Guilhem; Kopylov, Andrey; Tran, Thi Thu Thao

doi:10.3906/elk-1803-100

Cited by 11 publications

(14 citation statements)

References 16 publications

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“…We show the performance of our proposed method for restoring images contaminated with mixed Poisson-Gaussian noise. Noisy observations are generated by Poisson noise with some fixed peak I max , and by Gaussian noise with standard deviation σ G to the test images (see [Pham, 2018] for more details).…”

Section: Resultsmentioning

confidence: 99%

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An adaptive algorithm for restoring image corrupted by mixed noise

Thang

Thảo

Phan

et al. 2019

CAP

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Image denoising is one of the fundamental problems in image processing. Digital images are often contaminated by noise due to the image acquisition process under poor conditions. In this paper, we propose an effective approach to remove mixed Poisson-Gaussian noise in digital images. Particularly, we propose to use a spatially adaptive total variation regularization term in order to enhance the ability of edge preservation. We also propose an instance of the alternating direction algorithm to solve the proposed denoising model as an optimization problem. The experiments on popular natural images demonstrate that our approach achieves superior accuracy than other recent state-of-the-art techniques.

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Section: Resultsmentioning

confidence: 99%

“…In general, image restoration is often formulated as the problem of reconstructing a true image u with the size of (M × N ) corrupted by random * Corresponding author noise η, from an observed image f . The sought-for image u is a solution of the corresponding inverse problem [Pham, 2015;Pham, 2018].…”

Section: Introductionmentioning

confidence: 99%

An adaptive algorithm for restoring image corrupted by mixed noise

Thang

Thảo

Phan

et al. 2019

CAP

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“…Initialize: images obtained other well known methods, such as TV-L 1 (Chambolle et al, 2010), the Modified scheme for Mixed Poisson-Gaussian model (MS-MPG) (Pham et al, 2018) and the Bregman method (Goldstein and Osher, 2009). All of the compared methods perform image denoising with their optimal parameters.…”

Section: Implementation Issuesmentioning

confidence: 99%

“…Gaussian (Rudin et al, 1992;Pham and Kopylov, 2015), Poisson (Chan and Shen, 2005;Le et al, 2007), Cauchy (Sciacchitano et al, 2015), as well as some mixed noise models, e.g. mixed Gaussian-Impulse noise (Yan, 2013), mixed Gaussian-Salt and Pepper noise (Liu et al, 2017), mixed Poisson-Gaussian (Calatroni et al, 2017;Pham et al, 2018;Tran et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Total Variation Minimization Method for Image Restoration

Pham¹,

Tran²,

Gamard³

2020

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In this paper, we present an effective algorithm for solving the Poisson-Gaussian total variation model. The existence and uniqueness of solution for the mixed Poisson-Gaussian model are proved. Due to the strict convexity of the model, the split-Bregman method is employed to solve the minimization problem. Experimental results show the effectiveness of the proposed method for mixed Poisson-Gaussion noise removal. Comparison with other existing and well-known methods is provided as well.

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Combined total variation of first and fractional orders for Poisson noise removal in digital images

Pham

Tran

Pham

et al. 2021

ICS

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Introduction: Many methods have been proposed to handle the image restoration problem with Poisson noise. A popular approach to Poissonian image reconstruction is the one based on Total Variation. This method can provide significantly sharp edges and visually fine images, but it results in piecewise-constant regions in the resulting images. Purpose: Developing an adaptive total variation-based model for the reconstruction of images contaminated by Poisson noise, and an algorithm for solving the optimization problem. Results: We proposed an effective way to restore images degraded by Poisson noise. Using the Bayesian framework, we proposed an adaptive model based on a combination of first-order total variation and fractional order total variation. The first-order total variation model is efficient for suppressing the noise and preserving the keen edges simultaneously. However, the first-order total variation method usually causes artifact problems in the obtained results. To avoid this drawback, we can use high-order total variation models, one of which is the fractional-order total variation-based model for image restoration. In the fractional-order total variation model, the derivatives have an order greater than or equal to one. It leads to the convenience of computation with a compact discrete form. However, methods based on the fractional-order total variation may cause image blurring. Thus, the proposed model incorporates the advantages of two total variation regularization models, having a significant effect on the edge-preserving image restoration. In order to solve the considered optimization problem, the Split Bregman method is used. Experimental results are provided, demonstrating the effectiveness of the proposed method. Practical relevance: The proposed method allows you to restore Poissonian images preserving their edges. The presented numerical simulation demonstrates the competitive performance of the model proposed for image reconstruction. Discussion: From the experimental results, we can see that the proposed algorithm is effective in suppressing noise and preserving the image edges. However, the weighted parameters in the proposed model were not automatically selected at each iteration of the proposed algorithm. This requires additional research.

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An algorithm for image restoration with mixed noise using total variation regularization

Cited by 11 publications

References 16 publications

An adaptive algorithm for restoring image corrupted by mixed noise

An adaptive algorithm for restoring image corrupted by mixed noise

An Efficient Total Variation Minimization Method for Image Restoration

Combined total variation of first and fractional orders for Poisson noise removal in digital images

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