Image denoising method based on curvelet transform and total variation

被引：0

作者：

Ni, Xue ^{[1
]}

Li, Qingwu ^{[1
]}

Meng, Fan ^{[1
]}

Shi, Dan ^{[1
]}

Fan, Xinnan ^{[1
]}

机构：

[1] College of Computer and Information Engineering, Hohai University, Changzhou

来源：

Guangxue Xuebao/Acta Optica Sinica | 2009年 / 29卷 / 09期

关键词：

Curvelet transform; Image denoising; Image fusion; Image processing; Total variation;

D O I：

10.3788/AOS20092909.2390

中图分类号：

学科分类号：

摘要：

Curvelet transform can preserve more details for image denoising, but it always has the 'warp-around' artifacts in image edges. Total variation, another effective image denoising method, can preserve edges better, but image texture information will be also smoothed. An efficient image denoising method based on combination of curvelet transform and total variation is proposed. Firstly, the image is denoised by curvelet thresholding method and total variation method. Then, the two denoised images are fused using curvelet transform. Here the weighted average algorithm and maximizing absolute value algorithm are used respectively to process the low-frequency coefficients and the high-frequency coefficients. Finally, the denoised image is reconstructed by the inverse curvelet transform. Experimental results show that the new method is effective in removing white noise, and the detail of the image is kept well. It has better denoising effect than single curvelet thresholding method and total variation method.

引用

页码：2390 / 2394

页数：4

共 16 条

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