J4 ›› 2013, Vol. 51 ›› Issue (02): 312-316.
Previous Articles Next Articles
LI Wei1, YANG Hang2
Received:
Online:
Published:
Contact:
Abstract:
The purpose of this letter is to develop a new curvelet denoising algorithm for denoising images corrupted with additive white Gaussian noise (AWGN). We used a total variation(TV) estimate as means to design a curveletdomain Wiener filter. The TV estimate indirectly yields the estimate of the image that is leveraged into the design of the filter. A peculiar aspect of this method is its use of TV and curvelet base, i.e., the TV for the design of the empirical Wiener filter and curvlet base for its application. Numerical examples demonstrate that our method can perform better than curvelet shrinkage and TVbased method. Curvelets have been mathematically proven to represent distributed discontinuities such as edges better than traditional wavelets.
Key words: image denoising; curvelet; Wiener filter
CLC Number:
LI Wei, YANG Hang. Curvelet Domain Empirical Wiener Filter[J].J4, 2013, 51(02): 312-316.
0 / / Recommend
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
URL: http://xuebao.jlu.edu.cn/lxb/EN/
http://xuebao.jlu.edu.cn/lxb/EN/Y2013/V51/I02/312
Cited