Wavelet-based image denoising with the normal inverse Gaussian prior and linear MMSE estimator

被引:22
作者
Bhuiyan, M. I. H. [1 ]
Ahmad, M. O. [1 ]
Swamy, M. N. S. [1 ]
机构
[1] Concordia Univ, Dept Elect & Comp Engn, Ctr Commun & Signal Proc, Montreal, PQ H3G 1M8, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
D O I
10.1049/iet-ipr:20070035
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A new spatially adaptive wavelet-based method is introduced for reducing noise in images corrupted by additive white Gaussian noise. It is shown that a symmetric normal inverse Gaussian distribution is highly suitable for modelling the wavelet coefficients. In order to estimate the parameters of the distribution, a maximum-likelihood-based technique is proposed, wherein the Gauss-Hermite quadrature approximation is exploited to perform the maximisation in a computationally efficient way. A Bayesian minimum mean-squared error (MMSE) estimator is developed utilising the proposed distribution. The variances corresponding to the noise-free coefficients are obtained from the Bayesian estimates using a local neighbourhood. A modified linear MMSE estimator that incorporates both intra-scale and inter-scale dependencies is proposed. The performance of the proposed method is studied using typical noise-free images corrupted with simulated noise and compared with that of the other state-of-the-art methods. It is shown that the proposed method gives higher values of the peak signal-to-noise ratio compared with most of the other denoising techniques and provides images of good visual quality. Also, the performance of the proposed method is quite close to that of the state-of-the-art Gaussian scale mixture (GSM) method, but with much less complexity.
引用
收藏
页码:203 / 217
页数:15
相关论文
共 42 条
[1]   Image denoising using bivariate α-stable distributions in the complex wavelet domain [J].
Achim, A ;
Kuruoglu, EE .
IEEE SIGNAL PROCESSING LETTERS, 2005, 12 (01) :17-20
[2]   SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling [J].
Achim, A ;
Tsakalides, P ;
Bezerianos, A .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003, 41 (08) :1773-1784
[3]  
[Anonymous], USC SIPI IM DAT
[4]   Spatially adaptive wavelet-based method using the Cauchy prior for denoising the SAR images [J].
Bhuiyan, M. I. H. ;
Ahmad, M. O. ;
Swamy, M. N. S. .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2007, 17 (04) :500-507
[5]   A new homomorphic Bayesian wavelet-based MMAE filter for despeckling SAR images [J].
Bhuiyan, MIH ;
Ahmad, MO ;
Swamy, MNS .
2005 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), VOLS 1-6, CONFERENCE PROCEEDINGS, 2005, :4935-4938
[6]  
BHUIYAN MIH, 2006, P NEWCAS, P75
[7]   Efficient wavelet-based image denoising algorithm [J].
Cai, ZH ;
Cheng, TH ;
Lu, C ;
Subramanian, KR .
ELECTRONICS LETTERS, 2001, 37 (11) :683-685
[8]   Spatially adaptive wavelet thresholding with context modeling for image denoising [J].
Chang, SG ;
Yu, B ;
Vetterli, M .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2000, 9 (09) :1522-1531
[9]   Adaptive wavelet thresholding for image denoising and compression [J].
Chang, SG ;
Yu, B ;
Vetterli, M .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2000, 9 (09) :1532-1546
[10]  
COIFMAN RR, 1995, WAVELETS STAT