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
Proximal Minimization Methods with Generalized Bregman Functions
SIAM Journal on Control and Optimization
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
Inverse Scale Space Theory for Inverse Problems
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Optimal estimation of deterioration from diagnostic image sequence
IEEE Transactions on Signal Processing
An iterative method with general convex fidelity term for image restoration
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Dual Norm Based Iterative Methods for Image Restoration
Journal of Mathematical Imaging and Vision
Dictionary learning based impulse noise removal via L1-L1 minimization
Signal Processing
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A generalized iterative regularization procedure based on the total variation penalization is introduced for image denoising models with non-quadratic convex fidelity terms. By using a suitable sequence of penalty parameters we solve the issue of solvability of minimization problems arising in each step of the iterative procedure, which has been encountered in a recently developed iterative total variation procedure Furthermore, we obtain rigorous convergence results for exact and noisy data.We test the behaviour of the algorithm on real images in several numerical experiments using L 1 and L 2 fitting terms. Moreover, we compare the results with other state-of-the art multiscale techniques for total variation based image restoration.