Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial adaptive Bayesian wavelet threshold exploiting scale and space consistency
Multidimensional Systems and Signal Processing
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
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
Sparse Long-Range Random Field and Its Application to Image Denoising
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Support vector regression based image denoising
Image and Vision Computing
A novel PDE based image restoration: Convection-diffusion equation for image denoising
Journal of Computational and Applied Mathematics
A moment-based nonlocal-means algorithm for image denoising
Information Processing Letters
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
Removal of correlated noise by modeling the signal of interest in the wavelet domain
IEEE Transactions on Image Processing
From Local Kernel to Nonlocal Multiple-Model Image Denoising
International Journal of Computer Vision
Edge structure preserving image denoising
Signal Processing
Expert Systems with Applications: An International Journal
Switching-based filter based on Dempster's combination rule for image processing
Information Sciences: an International Journal
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Evolution strategies based adaptive Lp LS-SVM
Information Sciences: an International Journal
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Feature-based wavelet shrinkage algorithm for image denoising
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Optimal Spatial Adaptation for Patch-Based Image Denoising
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
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion
IEEE Transactions on Image Processing
Efficient Nonlocal Means for Denoising of Textural Patterns
IEEE Transactions on Image Processing
Nonlinear Regularized Reaction-Diffusion Filters for Denoising of Images With Textures
IEEE Transactions on Image Processing
Image Modeling and Denoising With Orientation-Adapted Gaussian Scale Mixtures
IEEE Transactions on Image Processing
Multiresolution Bilateral Filtering for Image Denoising
IEEE Transactions on Image Processing
Blind Image Deconvolution Through Support Vector Regression
IEEE Transactions on Neural Networks
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For image denoising, the main challenge is how to preserve the information-bearing structures such as edges and textures to get satisfactory visual quality when improving the signal-to-noise-ratio (SNR). Edge-preserving image denoising has become a very intensive research topic. In this paper, we propose an image denoising using support vector machine (SVM) classification in nonsubsampled contourlet transform (NSCT) domain. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using the NSCT. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in NSCT domain, and the least squares support vector machine (LS-M) model is obtained by training. Then the NSCT detail coefficients are divided into two classes (edge-related coefficients and noise-related ones) by LS-SVM training model. Finally, the detail subbands of NSCT coefficients are denoised by using shrink method, in which the adaptive Bayesian 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.