Image denoising using a tight frame

被引:53
|
作者
Shen, LX [1 ]
Papadakis, M
Kakadiaris, IA
Konstantinidis, I
Kouri, D
Hoffman, D
机构
[1] Western Michigan Univ, Dept Math, Kalamazoo, MI 49008 USA
[2] Univ Houston, Dept Math, Houston, TX 77204 USA
[3] Univ Houston, Dept Comp Sci, Computat Biomed Lab, Houston, TX 77204 USA
[4] Univ Maryland, Dept Math, Norbert Wiener Ctr Harmon Anal & Applicat, College Pk, MD 20742 USA
[5] Univ Houston, Dept Chem, Houston, TX 77204 USA
[6] Univ Houston, Dept Phys, Houston, TX 77204 USA
[7] Iowa State Univ, Dept Chem, Ames, IA 50011 USA
[8] Iowa State Univ, Ames Lab, Ames, IA 50011 USA
基金
美国国家科学基金会;
关键词
image denoising; tight frame; wavelets;
D O I
10.1109/TIP.2005.864240
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present a general mathematical theory for lifting frames that allows us to modify existing filters to construct new ones that form Parseval frames. We apply our theory to design nonseparable Parseval frames from separable (tensor) products of a piecewise linear spline tight frame. These new frame systems incorporate the weighted average operator, the Sobel operator, and the Laplacian operator in directions that are integer multiples of 45 degrees. A new image denoising algorithm is then proposed, tailored to the specific properties of these new frame filters. We demonstrate the performance of our algorithm on a diverse set of images with very encouraging results.
引用
收藏
页码:1254 / 1263
页数:10
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