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
Convergence of an Iterative Method for Total Variation Denoising
SIAM Journal on Numerical Analysis
Computational Methods for Inverse Problems
Computational Methods for Inverse Problems
High-Order Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Iterative Image Restoration Combining Total Variation Minimization and a Second-Order Functional
International Journal of Computer Vision
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
On Semismooth Newton's Methods for Total Variation Minimization
Journal of Mathematical Imaging and Vision
An Algorithm for image removals and decompositions without inverse matrices
Journal of Computational and Applied Mathematics
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations
Fourth-order partial differential equations for noise removal
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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In this paper, we propose a modified fixed point iterative algorithm to solve the fourth-order PDE model for image restoration problem. Compared with the standard fixed point algorithm, the proposed algorithm needn@?t to compute inverse matrices so that it can speed up the convergence and reduce the roundoff error. Furthermore, we prove the convergence of the proposed algorithm and give some experimental results to illustrate its effectiveness by comparing with the standard fixed point algorithm, the time marching algorithm and the split Bregman algorithm.