A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
Wavelet thresholding for multiple noisy image copies
IEEE Transactions on Image Processing
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications
IEEE Transactions on Image Processing
Multiscale LMMSE-based image denoising with optimal wavelet selection
IEEE Transactions on Circuits and Systems for Video Technology
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This paper proposes a nonsubsampled contourlet transform (NSCT) based multiscale linear minimum mean square-error estimation (LMMSE) scheme for image denoising. The contourlet transform is a new extension of the wavelet transform that provides a multi-resolution and multi-direction analysis for two dimension images. The NSCT expansion is composed of basis images oriented at various directions in multiple scales, with flexible aspect ratios. Given this rich set of basis images, the NSCT transform effectively captures smooth contours that are the dominant feature in natural images. To investigate the strong interscale dependencies of NSCT, we combine pixels at the same spatial location across scales as a vector and apply LMMSE to the vector. Experimental results show that the proposed approach outperforms wavelet method and contourlet based method both visually and in terns of the peak signal to noise ratio (PSNR) values at most cases.