A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Image denoising with complex ridgelets
Pattern Recognition
Automatic Estimation and Removal of Noise from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image denosing based on wavelet support vector regression
Machine Graphics & Vision International Journal
An Improved Adaptive Image Denoising Method Based on Multi-wavelet Transform
CCCM '08 Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 01
Image denoising in steerable pyramid domain based on a local Laplace prior
Pattern Recognition
Image De-noising Based on Multi-wavelet
IFITA '09 Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 03
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
The discrete wavelet transform: wedding the a trous and Mallatalgorithms
IEEE Transactions on Signal Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Sparse geometric image representations with bandelets
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications
IEEE Transactions on Image Processing
A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding
IEEE Transactions on Image Processing
A New Family of Nonredundant Transforms Using Hybrid Wavelets and Directional Filter Banks
IEEE Transactions on Image Processing
Image denoising with anisotropic bivariate shrinkage
Signal Processing
Computers and Electrical Engineering
Expert Systems with Applications: An International Journal
Image denoising using SVM classification in nonsubsampled contourlet transform domain
Information Sciences: an International Journal
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Image denoising is an important image processing task, both as itself, and as a preprocessing in image processing pipeline. The least squares support vector machine (LS-SVM) has shown to exhibit excellent classification performance in many applications. Based on undecimated discrete wavelet transform, a new wavelet-based image denoising using LS-SVM is proposed in this paper. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using the undecimated discrete wavelet transform. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in wavelet domain, and the LS-SVM model is obtained by training. Then the wavelet coefficients are divided into two classes (noisy coefficients and noise-free ones) by LS-SVM training model. Finally, all noisy wavelet coefficients are relatively well denoised by shrink method, in which the adaptive threshold is utilized. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.