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
SIAM Journal on Numerical Analysis
Reconstruction of Wavelet Coefficients Using Total Variation Minimization
SIAM Journal on Scientific Computing
A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision
Total variation blind deconvolution
IEEE Transactions on Image Processing
Fast, robust total variation-based reconstruction of noisy, blurred images
IEEE Transactions on Image Processing
Minimizing the total variation under a general convex constraint for image restoration
IEEE Transactions on Image Processing
Selective removal of impulse noise based on homogeneity level information
IEEE Transactions on Image Processing
Adaptive median filters: new algorithms and results
IEEE Transactions on Image Processing
Image Deblurring in the Presence of Impulsive Noise
International Journal of Computer Vision
An Algorithm for Adaptive Mean Filtering and Its Hardware Implementation
Journal of VLSI Signal Processing Systems
Total variation minimizing blind deconvolution with shock filter reference
Image and Vision Computing
Variational Deconvolution of Multi-channel Images with Inequality Constraints
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Journal of Mathematical Imaging and Vision
Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
Fast Two-Phase Image Deblurring Under Impulse Noise
Journal of Mathematical Imaging and Vision
Counter-examples for Bayesian MAP restoration
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Mumford-Shah regularizer with spatial coherence
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Reducing the area on a chip using a bank of evolved filters
ICES'07 Proceedings of the 7th international conference on Evolvable systems: from biology to hardware
An efficient two-phase L1-TV method for restoring blurred images with impulse noise
IEEE Transactions on Image Processing
An Augmented Lagrangian Method for TVg+L1-norm Minimization
Journal of Mathematical Imaging and Vision
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Anechoic Blind Source Separation Using Wigner Marginals
The Journal of Machine Learning Research
Wiener channel smoothing: robust wiener filtering of images
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Variational deblurring of images with uncertain and spatially variant blurs
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Color image deblurring with impulsive noise
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
Low-resolution image restoration using the combination method of sparse representation and PDE model
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Hybrid regularization image deblurring in the presence of impulsive noise
Journal of Visual Communication and Image Representation
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The problem of image deblurring in the presence of salt and pepper noise is considered. Standard image deconvolution algorithms, that are designed for Gaussian noise, do not perform well in this case. Median type filtering is a common method for salt and pepper noise removal. Deblurring an image that has been preprocessed by median-type filtering is however difficult, due to the amplification (in the deconvolution stage) of median-induced distortion. A unified variational approach to salt and pepper noise removal and image deblurring is presented. An objective functional that represents the goals of deblurring, noise-robustness and compliance with the piecewise-smooth image model is formulated. A modified L1 data fidelity term integrates deblurring with robustness to outliers. Elements from the Mumford-Shah functional, that favor piecewise smooth images with simple edge-sets, are used for regularization. Promising experimental results are shown for several blur models.