Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biased anisotropic diffusion—a unified regularization and diffusion approach to edge detection
ECCV 90 Proceedings of the first european conference on Computer vision
Image selective smoothing and edge detection by nonlinear diffusion
SIAM Journal on Numerical Analysis
Constrained Restoration and the Recovery of Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
International Journal of Computer Vision
SIAM Journal on Numerical Analysis
Coherence-Enhancing Diffusion Filtering
International Journal of Computer Vision
Variational Restoration and Edge Detection for Color Images
Journal of Mathematical Imaging and Vision
Reconstruction of Wavelet Coefficients Using Total Variation Minimization
SIAM Journal on Scientific Computing
A Common Framework for Curve Evolution, Segmentation and Anisotropic Diffusion
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision
$\Gamma$-Convergence of Discrete Functionals with Nonconvex Perturbation for Image Classification
SIAM Journal on Numerical Analysis
Image deblurring in the presence of salt-and-pepper noise
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
A general framework for low level vision
IEEE Transactions on Image Processing
Total variation blind deconvolution
IEEE Transactions on Image Processing
Variational approach for edge-preserving regularization using coupled PDEs
IEEE Transactions on Image Processing
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
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
IEEE Transactions on Image Processing
Adaptive median filters: new algorithms and results
IEEE Transactions on Image Processing
Coarse to over-fine optical flow estimation
Pattern Recognition
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Nonlocal Variational Image Deblurring Models in the Presence of Gaussian or Impulse Noise
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
An Adaptive Method for Recovering Image from Mixed Noisy Data
International Journal of Computer Vision
Color Image Restoration Using Nonlocal Mumford-Shah Regularizers
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Fast Two-Phase Image Deblurring Under Impulse Noise
Journal of Mathematical Imaging and Vision
Mumford-Shah regularizer with spatial coherence
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Blind and semi-blind deblurring of natural images
IEEE Transactions on Image Processing
Generalised Nonlocal Image Smoothing
International Journal of Computer Vision
Adaptive Variational Method for Restoring Color Images with High Density Impulse Noise
International Journal of Computer Vision
An efficient two-phase L1-TV method for restoring blurred images with impulse noise
IEEE Transactions on Image Processing
Restoration of images corrupted with blur and impulse noise
Proceedings of the 2011 International Conference on Communication, Computing & Security
Image restoration under mixed noise using globally convex segmentation
Journal of Visual Communication and Image Representation
The beltrami-mumford-shah functional
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
From a modified ambrosio-tortorelli to a randomized part hierarchy tree
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Robust image deblurring using hyper laplacian model
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Hybrid regularization image deblurring in the presence of impulsive noise
Journal of Visual Communication and Image Representation
From a Non-Local Ambrosio-Tortorelli Phase Field to a Randomized Part Hierarchy Tree
Journal of Mathematical Imaging and Vision
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Consider the problem of image deblurring in the presence of impulsive noise. Standard image deconvolution methods rely on the Gaussian noise model and do not perform well with impulsive noise. The main challenge is to deblur the image, recover its discontinuities and at the same time remove the impulse noise. Median-based approaches are inadequate, because at high noise levels they induce nonlinear distortion that hampers the deblurring process. Distinguishing outliers from edge elements is difficult in current gradient-based edge-preserving restoration methods. The suggested approach integrates and extends the robust statistics, line process (half quadratic) and anisotropic diffusion points of view. We present a unified variational approach to image deblurring and impulse noise removal. The objective functional consists of a fidelity term and a regularizer. Data fidelity is quantified using the robust modified L 1 norm, and elements from the Mumford-Shah functional are used for regularization. We show that the Mumford-Shah regularizer can be viewed as an extended line process. It reflects spatial organization properties of the image edges, that do not appear in the common line process or anisotropic diffusion. This allows to distinguish outliers from edges and leads to superior experimental results.