Below the Surface of the Non-local Bayesian Image Denoising Method

被引:1
|
作者
Arias, Pablo [1 ]
Nikolova, Mila [2 ]
机构
[1] Univ Paris Saclay, ENS Cachan, CMLA, F-94235 Cachan, France
[2] Univ Paris Saclay, CNRS, ENS Cachan, CMLA, F-94235 Cachan, France
来源
SCALE SPACE AND VARIATIONAL METHODS IN COMPUTER VISION, SSVM 2017 | 2017年 / 10302卷
关键词
ALGORITHM;
D O I
10.1007/978-3-319-58771-4_17
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The non-local Bayesian (NLB) patch-based approach of Lebrun et al. [12] is considered as a state-of-the-art method for the restoration of (color) images corrupted by white Gaussian noise. It gave rise to numerous ramifications like e.g., possible improvements, processing of various data sets and video. This article is the first attempt to analyse the method in depth in order to understand the main phenomena underlying its effectiveness. Our analysis, corroborated by numerical tests, shows several unexpected facts. In a variational setting, the first-step Bayesian approach to learn the prior for patches is equivalent to a pseudo-Tikhonov regularisation where the regularisation parameters can be positive or negative. Practically very good results in this step are mainly due to the aggregation stage - whose importance needs to be re-evaluated.
引用
收藏
页码:208 / 220
页数:13
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