Volume 1, Issue 5
An Efficient Operator-Splitting Method for Noise Removal in Images

D. Krishnan, P. Lin & X.-C. Tai

DOI:

Commun. Comput. Phys., 1 (2006), pp. 847-858.

Published online: 2006-01

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  • Abstract

In this work, noise removal in digital images is investigated. The importance of this problem lies in the fact that removal of noise is a necessary pre-processing step for other image processing tasks such as edge detection, image segmentation, image compression, classification problems, image registration etc. A number of different approaches have been proposed in the literature. In this work, a non-linear PDE-based algorithm is developed based on the ideas proposed by Lysaker, Osher and Tai [IEEE Trans. Image Process., 13 (2004), 1345-1357] . This algorithm consists of two steps: flow field smoothing of the normal vectors, followed by image reconstruction. We propose a finite-difference based additive operator-splitting method that allows for much larger time-steps. This results in an efficient method for noise-removal that is shown to have good visual results. The energy is studied as an objective measure of the algorithm performance.

  • Keywords

Noise removal nonlinear PDEs additive operator splitting (AOS).

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@Article{CiCP-1-847, author = {}, title = {An Efficient Operator-Splitting Method for Noise Removal in Images}, journal = {Communications in Computational Physics}, year = {2006}, volume = {1}, number = {5}, pages = {847--858}, abstract = {

In this work, noise removal in digital images is investigated. The importance of this problem lies in the fact that removal of noise is a necessary pre-processing step for other image processing tasks such as edge detection, image segmentation, image compression, classification problems, image registration etc. A number of different approaches have been proposed in the literature. In this work, a non-linear PDE-based algorithm is developed based on the ideas proposed by Lysaker, Osher and Tai [IEEE Trans. Image Process., 13 (2004), 1345-1357] . This algorithm consists of two steps: flow field smoothing of the normal vectors, followed by image reconstruction. We propose a finite-difference based additive operator-splitting method that allows for much larger time-steps. This results in an efficient method for noise-removal that is shown to have good visual results. The energy is studied as an objective measure of the algorithm performance.

}, issn = {1991-7120}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/cicp/7982.html} }
TY - JOUR T1 - An Efficient Operator-Splitting Method for Noise Removal in Images JO - Communications in Computational Physics VL - 5 SP - 847 EP - 858 PY - 2006 DA - 2006/01 SN - 1 DO - http://dor.org/ UR - https://global-sci.org/intro/article_detail/cicp/7982.html KW - Noise removal KW - nonlinear PDEs KW - additive operator splitting (AOS). AB -

In this work, noise removal in digital images is investigated. The importance of this problem lies in the fact that removal of noise is a necessary pre-processing step for other image processing tasks such as edge detection, image segmentation, image compression, classification problems, image registration etc. A number of different approaches have been proposed in the literature. In this work, a non-linear PDE-based algorithm is developed based on the ideas proposed by Lysaker, Osher and Tai [IEEE Trans. Image Process., 13 (2004), 1345-1357] . This algorithm consists of two steps: flow field smoothing of the normal vectors, followed by image reconstruction. We propose a finite-difference based additive operator-splitting method that allows for much larger time-steps. This results in an efficient method for noise-removal that is shown to have good visual results. The energy is studied as an objective measure of the algorithm performance.

D. Krishnan, P. Lin & X.-C. Tai. (2020). An Efficient Operator-Splitting Method for Noise Removal in Images. Communications in Computational Physics. 1 (5). 847-858. doi:
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