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
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
SIAM Journal on Numerical Analysis
Total variation based convex filters for medical imaging
Applied Mathematics and Computation
Acceleration Methods for Total Variation-Based Image Denoising
SIAM Journal on Scientific Computing
A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision
Second-order Cone Programming Methods for Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
Journal of Scientific Computing
Journal of Mathematical Imaging and Vision
A comparison of three total variation based texture extraction models
Journal of Visual Communication and Image Representation
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Augmented Lagrangian Method, Dual Methods and Split Bregman Iteration for ROF Model
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Digital filters as absolute norm regularizers
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Noise removal with Gauss curvature-driven diffusion
IEEE Transactions on Image Processing
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In this paper, the geometry and scale selection properties of the total variation (TV) regularized L^p-model are rigorously analyzed. Some intrinsic features different from the TV-L^1 model are derived and demonstrated. Numerical algorithms based on recent developed augmented Lagrangian methods are implemented and numerical results consistent with the theoretical results are provided.