The nature of statistical learning theory
The nature of statistical learning theory
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A new fuzzy logic filter for image enhancement
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Gray-scale image enhancement as an automatic process driven by evolution
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
Spatial noise shaping based on human visual sensitivity and its application to image coding
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Spatially adaptive wavelet denoising using the minimum description length principle
IEEE Transactions on Image Processing
Noise removal using smoothed normals and surface fitting
IEEE Transactions on Image Processing
A steerable complex wavelet construction and its application to image denoising
IEEE Transactions on Image Processing
Image denoising based on wavelets and multifractals for singularity detection
IEEE Transactions on Image Processing
Adaptive kernel-based image denoising employing semi-parametric regularization
IEEE Transactions on Image Processing
A wavelet-based image denoising using least squares support vector machine
Engineering Applications of Artificial Intelligence
Document image analysis: issues, comparison of methods and remaining problems
Artificial Intelligence Review
Adaptive medical image denoising using support vector regression
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Image denoising using SVM classification in nonsubsampled contourlet transform domain
Information Sciences: an International Journal
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Support vector regression (SVR) has been applied for blind image deconvolution. In this correspondence, it is applied in the problem of image denoising. After training on noisy images with ground-truth, support vectors (SVs) are identified and their weights are computed. Then the SVs and their weights are used in denoising different images corrupted by random noise at different levels on a pixel-by-pixel basis. The proposed SVR based image denoising algorithm is an example-based approach since it uses SVs in denoising. The SVR denoising is compared with a multiple wavelet domain method (Besov ball projection). Some initial experiments indicate that SVR based image denoising outperforms Besov ball projection method on non-natural images (e.g. document images) in terms of both peak signal-to-noise ratio (PSNR) and visual inspection.