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
Support vector regression based image denoising
Image and Vision Computing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Wavelet thresholding of multivalued images
IEEE Transactions on Image Processing
Sparse geometric image representations with bandelets
IEEE Transactions on Image Processing
Feature-based wavelet shrinkage algorithm for image denoising
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 by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Engineering Applications of Artificial Intelligence
SR-NBS: A fast sparse representation based N-best class selector for robust phoneme classification
Engineering Applications of Artificial Intelligence
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The least squares support vector machine (LS-SVM) is a modified version of SVM, which uses the equality constraints to replace the original convex quadratic programming problem. Consequently, the global minimizer is much easier to obtain in LS-SVM by solving the set of linear equation. LS-SVM has shown to exhibit excellent classification performance in many applications. In this paper, a wavelet-based image denoising using LS-SVM is proposed. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using the 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 soft-thresholding method. 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.