Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Digital Image Processing
Reconstruction of Wavelet Coefficients Using Total Variation Minimization
SIAM Journal on Scientific Computing
Iterative Kernel Principal Component Analysis for Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Impulsive noise suppression from images by using Anfis interpolant and lillietest
EURASIP Journal on Applied Signal Processing
Minimization of a Detail-Preserving Regularization Functional for Impulse Noise Removal
Journal of Mathematical Imaging and Vision
Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation
International Journal of Computer Vision
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Support vector regression based image denoising
Image and Vision Computing
Journal of Mathematical Imaging and Vision
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
Online Kernel-Based Classification Using Adaptive Projection Algorithms
IEEE Transactions on Signal Processing - Part I
A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning
IEEE Transactions on Signal Processing
A new efficient approach for the removal of impulse noise from highly corrupted images
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
Image restoration subject to a total variation constraint
IEEE Transactions on Image Processing
A universal noise removal algorithm with an impulse detector
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
PDE-based image restoration: a hybrid model and color image denoising
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
The complex Gaussian kernel LMS algorithm
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Edge preserving image denoising with a closed form solution
Pattern Recognition
Dictionary learning based impulse noise removal via L1-L1 minimization
Signal Processing
Learning with limited and noisy tagging
Proceedings of the 21st ACM international conference on Multimedia
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The main contribution of this paper is the development of a novel approach, based on the theory of Reproducing Kernel Hilbert Spaces (RKHS), for the problem of noise removal in the spatial domain. The proposed methodology has the advantage that it is able to remove any kind of additive noise (impulse, gaussian, uniform, etc.) from any digital image, in contrast to the most commonly used denoising techniques, which are noise dependent. The problem is cast as an optimization task in a RKHS, by taking advantage of the celebrated Representer Theorem in its semi-parametric formulation. The semi-parametric formulation, although known in theory, has so far found limited, to our knowledge, application. However, in the image denoising problem, its use is dictated by the nature of the problem itself. The need for edge preservation naturally leads to such a modeling. Examples verify that in the presence of gaussian noise the proposed methodology performs well compared to wavelet based technics and outperforms them significantly in the presence of impulse or mixed noise.