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
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
Images as Embedded Maps and Minimal Surfaces: Movies, Color, Texture, and Volumetric Medical Images
International Journal of Computer Vision - Special issue on computer vision research at the Technion
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
Vector-Valued Image Regularization with PDEs: A Common Framework for Different Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative Image Restoration Combining Total Variation Minimization and a Second-Order Functional
International Journal of Computer Vision
Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing
SIAM Journal on Imaging Sciences
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Anisotropic diffusion of multivalued images with applications to color filtering
IEEE Transactions on Image Processing
Color TV: total variation methods for restoration of vector-valued images
IEEE Transactions on Image Processing
Fourth-order partial differential equations for noise removal
IEEE Transactions on Image Processing
The digital TV filter and nonlinear denoising
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
SIAM Journal on Imaging Sciences
Operator Splittings, Bregman Methods and Frame Shrinkage in Image Processing
International Journal of Computer Vision
Wavelet frame based surface reconstruction from unorganized points
Journal of Computational Physics
A combined segmentation and registration framework with a nonlinear elasticity smoother
Computer Vision and Image Understanding
A Fast Algorithm for Euler's Elastica Model Using Augmented Lagrangian Method
SIAM Journal on Imaging Sciences
Geometry of total variation regularized Lp-model
Journal of Computational and Applied Mathematics
Journal of Scientific Computing
Augmented Lagrangian Method for Generalized TV-Stokes Model
Journal of Scientific Computing
Domain decomposition methods with graph cuts algorithms for total variation minimization
Advances in Computational Mathematics
A fast augmented lagrangian method for euler's elastica model
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
Robust edge detection using mumford-shah model and binary level set method
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Split Bregman iteration and infinity Laplacian for image decomposition
Journal of Computational and Applied Mathematics
Journal of Visual Communication and Image Representation
Fast regularization of matrix-valued images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Group-Valued regularization for analysis of articulated motion
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
X-Ray CT Image Reconstruction via Wavelet Frame Based Regularization and Radon Domain Inpainting
Journal of Scientific Computing
An Efficient Algorithm for l0 Minimization in Wavelet Frame Based Image Restoration
Journal of Scientific Computing
Polyakov action minimization for efficient color image processing
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Variational structure-texture image decomposition on manifolds
Signal Processing
Nonlinear multigrid method for solving the anisotropic image denoising models
Numerical Algorithms
Coupling Image Restoration and Segmentation: A Generalized Linear Model/Bregman Perspective
International Journal of Computer Vision
A fixed-point augmented Lagrangian method for total variation minimization problems
Journal of Visual Communication and Image Representation
Computers in Biology and Medicine
A Gauss-Newton approach to joint image registration and intensity correction
Computer Methods and Programs in Biomedicine
A simple and efficient algorithm for fused lasso signal approximator with convex loss function
Computational Statistics
An effective dual method for multiplicative noise removal
Journal of Visual Communication and Image Representation
A coupled variational model for image denoising using a duality strategy and split Bregman
Multidimensional Systems and Signal Processing
A Splitting Method for Orthogonality Constrained Problems
Journal of Scientific Computing
Hi-index | 0.01 |
In the recent decades the ROF model (total variation (TV) minimization) has made great successes in image restoration due to its good edge-preserving property. However, the non-differentiability of the minimization problem brings computational difficulties. Different techniques have been proposed to overcome this difficulty. Therein methods regarded to be particularly efficient include dual methods of CGM (Chan, Golub, and Mulet) [7] Chambolle [6] and split Bregman iteration [14], as well as splitting-and-penalty based method [28] [29]. In this paper, we show that most of these methods can be classified under the same framework. The dual methods and split Bregman iteration are just different iterative procedures to solve the same system resulted from a Lagrangian and penalty approach. We only show this relationship for the ROF model. However, it provides a uniform framework to understand these methods for other models. In addition, we provide some examples to illustrate the accuracy and efficiency of the proposed algorithm.