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
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
Total variation image restoration: numerical methods and extensions
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Dual Norms and Image Decomposition Models
International Journal of Computer Vision
Iterative Image Restoration Combining Total Variation Minimization and a Second-Order Functional
International Journal of Computer Vision
Simultaneous structure and texture image inpainting
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Total-Variation Based Piecewise Affine Regularization
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Proximal Algorithms for Multicomponent Image Recovery Problems
Journal of Mathematical Imaging and Vision
Total Variation as a Local Filter
SIAM Journal on Imaging Sciences
Multiscale Texture Extraction with Hierarchical (BV,Gp,L2) Decomposition
Journal of Mathematical Imaging and Vision
A Combined First and Second Order Variational Approach for Image Reconstruction
Journal of Mathematical Imaging and Vision
A Framework for Moving Least Squares Method with Total Variation Minimizing Regularization
Journal of Mathematical Imaging and Vision
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This paper proposes a natural and efficient way to achieve staircase reduction in texture extraction models of image processing. Moreover, we propose a precise framework for this amalgamation. In a sense, we utilize the best of both worlds: (I) the use of higher order derivatives through a variant of the Chambolle-Lions inf convolution energy (an image decomposition model in itself) along with (II) approximations to Meyer's G and E norms including the H^-^1 negative norm for ameliorating staircasing in image decomposition and restoration problems.