Generalized cross validation for wavelet thresholding
Signal Processing
Digital Image Restoration
Data analytic wavelet threshold selection in 2-D signal denoising
IEEE Transactions on Signal Processing
The phaselet transform-an integral redundancy nearly shift-invariant wavelet transform
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Wavelet shrinkage and generalized cross validation for image denoising
IEEE Transactions on Image Processing
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Wavelets, Ridgelets, and Curvelets for Poisson Noise Removal
IEEE Transactions on Image Processing
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In this paper, we propose a multilevel thresholding technique for noise removal in curvelet transform domain which uses cycle-spinning. Most of uncorrelated noise gets removed by thresholding curvelet coefficients at lowest level, while correlated noise gets removed by only a fraction at lower levels, so we used multilevel thresholding on curvelet coefficients. The threshold in the proposed method depends on the variance of curvelet coefficients, the mean and the median of absolute curvelet coefficients at a particular level which makes it adaptive in nature. Results obtained for 2-D images demonstrate an Improved performance over other recent related methods available in literature.