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
Iterative methods for total variation denoising
SIAM Journal on Scientific Computing - Special issue on iterative methods in numerical linear algebra; selected papers from the Colorado conference
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision
Image Deblurring in the Presence of Impulsive Noise
International Journal of Computer Vision
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
An Adaptive Method for Recovering Image from Mixed Noisy Data
International Journal of Computer Vision
An Efficient TVL1 Algorithm for Deblurring Multichannel Images Corrupted by Impulsive Noise
SIAM Journal on Scientific Computing
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Fast Two-Phase Image Deblurring Under Impulse Noise
Journal of Mathematical Imaging and Vision
Fast, robust total variation-based reconstruction of noisy, blurred images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization
IEEE Transactions on Image Processing
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The total variation based regularization method has been proven to be quite efficient for image restoration. However, the noise in the image is assumed to be Gaussian in the overwhelming majority of researches. In this paper, an extended ROF model is presented to restore image with non-Gaussian noise, in which the locations of the blurred pixels with high level noise are detected by a function and two estimated parameters of noise, while the fidelity and smoothness terms can be adaptively adjusted by updating these parameters. In contrast to the previous method, our model can give a much better restoration in some particular cases, such as the blurred image corrupted by impulsive noise and mixed noise. Moreover, the proposed minimization problem is solved by the split Bregman iteration, which makes our algorithm very fast. We provide some experiments and comparisons with other methods to illustrate the high efficiency of our method.