Iterative methods for total variation denoising

被引：803

作者：

Vogel, CR ^{[1
]}

Oman, ME ^{[1
]}

机构：

[1] IOWA STATE UNIV SCI & TECHNOL, US DOE, AMES LAB, SCALABLE COMP LAB, AMES, IA 50011 USA

来源：

SIAM JOURNAL ON SCIENTIFIC COMPUTING | 1996年 / 17卷 / 01期

关键词：

total variation; denoising; image reconstruction; multigrid methods; confocal microscopy; fixed point iteration;

D O I：

10.1137/0917016

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Total variation (TV) methods are very effective for recovering ''blocky,'' possibly discontinuous, images from noisy data. A fixed point algorithm for minimizing a TV-penalized least squares functional is presented and compared with existing minimization schemes. A variant of the cell-centered finite difference multigrid method of Ewing and Shen is implemented for solving the (large, sparse) linear subproblems. Numerical results are presented for one- and two-dimensional examples; in particular, the algorithm is applied to actual data obtained from confocal microscopy.

引用

页码：227 / 238

页数：12

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