Image denoising using a multivariate shrinkage function in the curvelet domain

A new method based on the curvelet transform is proposed for image denoising. This method exploits a multivariate generalized spherically contoured exponential (GSCE) probability density function to model neighboring curvelet coefficients. Based on the multivariate probability model, which takes account of the dependency between the estimated curvelet coefficients and their neighbors, a multivariate shrinkage function for image denoising is derived by maximum a posteriori (MAP) estimator. Experimental results show that the proposed method obtains better performance than the existing curvelet-based image denoising method.

[1]  Thierry Blu,et al.  A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding , 2007, IEEE Transactions on Image Processing.

[2]  Aleksandra Pizurica,et al.  Analysis of the Statistical Dependencies in the Curvelet Domain and Applications in Image Compression , 2007, ACIVS.

[3]  Laurent Demanet,et al.  Fast Discrete Curvelet Transforms , 2006, Multiscale Model. Simul..

[4]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[5]  E. Candès,et al.  New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities , 2004 .

[6]  Emmanuel J. Candès,et al.  The curvelet transform for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[7]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[8]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[9]  Levent Sendur,et al.  A bivariate shrinkage function for wavelet-based denoising , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.