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Volume 16, Issue 2
Adaptive Parameter Selection for Preserving Edges Based on EPLL

Ze Qin & Xiulan Sheng

J. Info. Comput. Sci. , 16 (2021), pp. 098-107.

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  • Abstract

Though image denoising has experienced rapid development, there remain problems to be solved such as preserving the edge and meaningful details in image denoising. In this paper, we focus on this hot issue. Considering the parameter in original method is a constant, we introduce a new adaptive parameter selection based on EPLL (Expected Patch Log Likelihood) by the use of image gradient and the local variance, which varies with different regions of the image. What’s more, for solving staircase effect which common in anisotropic diffusion models, we add a gradient fidelity term to release it. The experiment shows that our proposed method proves the effectiveness not only in vision but also on quantitative evaluation.

  • Keywords

Image denoising, adaptive parameter, expected patch log likelihood, edges.

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COPYRIGHT: © Global Science Press

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@Article{JICS-16-098, author = {Qin , Ze and Sheng , Xiulan}, title = {Adaptive Parameter Selection for Preserving Edges Based on EPLL}, journal = {Journal of Information and Computing Science}, year = {2021}, volume = {16}, number = {2}, pages = {098--107}, abstract = {

Though image denoising has experienced rapid development, there remain problems to be solved such as preserving the edge and meaningful details in image denoising. In this paper, we focus on this hot issue. Considering the parameter in original method is a constant, we introduce a new adaptive parameter selection based on EPLL (Expected Patch Log Likelihood) by the use of image gradient and the local variance, which varies with different regions of the image. What’s more, for solving staircase effect which common in anisotropic diffusion models, we add a gradient fidelity term to release it. The experiment shows that our proposed method proves the effectiveness not only in vision but also on quantitative evaluation.

}, issn = {3080-180X}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22367.html} }
TY - JOUR T1 - Adaptive Parameter Selection for Preserving Edges Based on EPLL AU - Qin , Ze AU - Sheng , Xiulan JO - Journal of Information and Computing Science VL - 2 SP - 098 EP - 107 PY - 2021 DA - 2021/06 SN - 16 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22367.html KW - Image denoising, adaptive parameter, expected patch log likelihood, edges. AB -

Though image denoising has experienced rapid development, there remain problems to be solved such as preserving the edge and meaningful details in image denoising. In this paper, we focus on this hot issue. Considering the parameter in original method is a constant, we introduce a new adaptive parameter selection based on EPLL (Expected Patch Log Likelihood) by the use of image gradient and the local variance, which varies with different regions of the image. What’s more, for solving staircase effect which common in anisotropic diffusion models, we add a gradient fidelity term to release it. The experiment shows that our proposed method proves the effectiveness not only in vision but also on quantitative evaluation.

Qin , Ze and Sheng , Xiulan. (2021). Adaptive Parameter Selection for Preserving Edges Based on EPLL. Journal of Information and Computing Science. 16 (2). 098-107. doi:
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