A New Nonlocal H 1 Model for Image Denoising

被引:14
|
作者
Jin, Yan [1 ]
Jost, Juergen [2 ,3 ]
Wang, Guofang [4 ]
机构
[1] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310023, Zhejiang, Peoples R China
[2] Max Planck Inst Math Sci, D-04103 Leipzig, Germany
[3] Univ Leipzig, Fak Math & Informat, D-04091 Leipzig, Germany
[4] Univ Freiburg, Math Inst, D-79104 Freiburg, Germany
关键词
Image denoising; Nonlocal means filter; Nonlocal operator; Nonlocal TV; Nonlocal H-1; REGULARIZATION;
D O I
10.1007/s10851-012-0395-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Following ideas of Kindermann et al. (Multiscale Model. Simul. 4(4):1091-1115, 2005) and Gilboa and Osher (Multiscale Model. Simul. 7:1005-1028, 2008) we introduce new nonlocal operators to interpret the nonlocal means filter (NLM) as a regularization of the corresponding Dirichlet functional. Then we use these nonlocal operators to propose a new nonlocal H (1) model, which is (slightly) different from the nonlocal H (1) model of Gilboa and Osher (Multiscale Model. Simul. 6(2):595-630, 2007; Proc. SPIE 6498:64980U, 2007). The key point is that both the fidelity and the smoothing term are derived from the same geometric principle. We compare this model with the nonlocal H (1) model of Gilboa and Osher and the nonlocal means filter, both theoretically and in computer experiments. The experiments show that this new nonlocal H (1) model also provides good results in image denoising and closer to the nonlocal means filter than the H (1) model of Gilboa and Osher. This means that the new nonlocal operators yield a better interpretation of the nonlocal means filter than the nonlocal operators given in Gilboa and Osher (Multiscale Model. Simul. 7:1005-1028, 2008).
引用
收藏
页码:93 / 105
页数:13
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