Wavelet domain image denoising for non-stationary noise and signal-dependent noise

被引：27

作者：

Goossens, Bart ^{[1
]}

Pizurica, Aleksandra ^{[1
]}

Philips, Wilfried ^{[1
]}

机构：

[1] Univ Ghent, Dept Telecommun & Informat Proc, Sint Pietersnieuwstr 41, B-9000 Ghent, Belgium

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS | 2006年

关键词：

image restoration; Gaussian noise; non-stationary noise; signal-dependent noise;

D O I：

10.1109/ICIP.2006.312694

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We develop a low-complexity overcomplete wavelet domain method for denoising digital images corrupted with non-stationary white additive Gaussian noise. The noise level for each pixel is estimated from a local window around that pixel. We use a shrinkage function that adapts itself to the noise level and to the spatially changing statistics of the image. Experiments show that this noise model has good results for different non-stationary noise sources. Finally, we extend our method for denoising images corrupted with signal-dependent noise.

引用

页码：1425 / +

页数：3

共 8 条

[1] Spatially adaptive wavelet thresholding with context modeling for image denoising [J].