Image denoising using normal inverse gaussian model in quaternion wavelet domain

被引:9
作者
Gai, Shan [1 ]
Luo, Limin [1 ]
机构
[1] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211102, Jiangsu, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Quaternion wavelet transform; Normal inverse Gaussian density; Maximum posterior estimator; Image denoising; SEGMENTATION; TRANSFORM; SPARSE;
D O I
10.1007/s11042-013-1812-2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a novel image denoising algorithm that can more effectively remove Gaussian white noise. The proposed algorithm is based on a design of a Maximum Posteriori Estimator (MAP) combined with a Quaternion Wavelet Transform (QWT) that utilizes the Normal Inverse Gaussian (NIG) Probability Density Function (PDF). The QWT is a near shift-invariant whose coefficients include one magnitude and three phase values. An NIG PDF which is specified by four real-value parameters is capable of modeling the heavy-tailed QWT coefficients, and describing the intra-scale dependency between the QWT coefficients. The NIG PDF is applied as a prior probability distribution, to model the coefficients by utilizing the Bayesian estimation technique. Additionally, a simple and fast method is given to estimate the parameters of the NIG PDF from the neighboring QWT coefficients. Experimental results show that the proposed method outperforms other existing denoising methods in terms of the PSNR, the structural similarity, and the edge preservation. It is clear that the proposed method can remove Gaussian white noise more effectively.
引用
收藏
页码:1107 / 1124
页数:18
相关论文
共 47 条
[21]   Locally adaptive wavelet domain Bayesian processor for denoising medical ultrasound images using Speckle modelling based on Rayleigh distribution [J].
Gupta, S ;
Chauhan, RC ;
Saxena, SC .
IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, 2005, 152 (01) :129-135
[22]  
Hamilton W.R., 1866, Elements of Quaternions
[23]   Color texture segmentation using quaternion-gabor filters [J].
Hui, Wang ;
Xiao-Hui, Wang ;
Yue, Zhou ;
Jie, Yang .
2006 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP 2006, PROCEEDINGS, 2006, :745-+
[24]   Sparse code shrinkage:: Denoising of nongaussian data by maximum likelihood estimation [J].
Hyvärinen, A .
NEURAL COMPUTATION, 1999, 11 (07) :1739-1768
[25]   Sparse geometric image representations with bandelets [J].
Le Pennec, E ;
Mallat, S .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2005, 14 (04) :423-438
[26]   Bayesian Inference on Multiscale Models for Poisson Intensity Estimation: Applications to Photon-Limited Image Denoising [J].
Lefkimmiatis, Stamatios ;
Maragos, Petros ;
Papandreou, George .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2009, 18 (08) :1724-1741
[27]   Pectoral muscle segmentation in mammograms based on homogenous texture and intensity deviation [J].
Li, Yanfeng ;
Chen, Houjin ;
Yang, Yongyi ;
Yang, Na .
PATTERN RECOGNITION, 2013, 46 (03) :681-691
[28]   Filters of wavelets on invariant sets for image denoising [J].
Lian, Qiaofang ;
Shen, Lixin ;
Xu, Yuesheng ;
Yang, Lihua .
APPLICABLE ANALYSIS, 2011, 90 (08) :1299-1322
[29]   SURE-LET multichannel image denoising: Interscale orthonormal wavelet thresholding [J].
Luisier, Florian ;
Blu, Thierry .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2008, 17 (04) :482-492
[30]   Image denoising using derotated complex wavelet coefficients [J].
Miller, Mark ;
Kingsbury, Nick .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2008, 17 (09) :1500-1511