A Spatially Adaptive Denoising Algorithm Based on Curvelet Transform
- DOI
- 10.2991/iccasm.2012.174How to use a DOI?
- Keywords
- Curvelet Transform, image denoising, Multiscale Geometric Analysis(MGA), CurShrink
- Abstract
A new approach for image denoising based on the Curvelet transform is presented in this paper. The existing theory for Curvelet and Ridgelet suggests that these new approaches can outperform wavelet method in certain image processing including image denoising, edge detection and image enhancement. However in original digital Curvelet transform it uses a simple hard-thresholding rule for filtering the noisy Curvelet coefficient which of course causes some problems such as killing too many signal Curvelet coefficients that might contain useful image information. Here we introduce BayesShrink denoising scheme into Curvelet domain that is an adaptive, data-driven thresholding approach for image denoising, namely CurShrink. The threshold is derived in a Bayesian framework, and the prior used on the Curvelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The approach is valid because Curvelet transform produce correlated Curvelet coefficients, along the edge or curve of the image; the large Curvelet coefficients maybe have large Curvelet coefficients as it neighbors. Experimental results show that the proposed method is better than hard-thresholding denoising scheme in wavelet and curvelet domain.
- Copyright
- © 2012, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Peng Feng AU - Feng Yang AU - Biao Wei AU - Deling Mi PY - 2012/08 DA - 2012/08 TI - A Spatially Adaptive Denoising Algorithm Based on Curvelet Transform BT - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012) PB - Atlantis Press SP - 685 EP - 689 SN - 1951-6851 UR - https://doi.org/10.2991/iccasm.2012.174 DO - 10.2991/iccasm.2012.174 ID - Feng2012/08 ER -