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
Mathematical Programming: Series A and B
Convergence Rates in Forward--Backward Splitting
SIAM Journal on Optimization
Reconstruction of Wavelet Coefficients Using Total Variation Minimization
SIAM Journal on Scientific Computing
Fields of Experts: A Framework for Learning Image Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Modeling Age Progression in Young Faces
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Split Bregman Algorithm, Douglas-Rachford Splitting and Frame Shrinkage
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Tetrolet transform: A new adaptive Haar wavelet algorithm for sparse image representation
Journal of Visual Communication and Image Representation
Fast image recovery using variable splitting and constrained optimization
IEEE Transactions on Image Processing
Hybrid compressive sampling via a new total variation TVL1
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
An EM algorithm for wavelet-based image restoration
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Majorization–Minimization Algorithms for Wavelet-Based Image Restoration
IEEE Transactions on Image Processing
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Image restoration is one of the most classical problems in image processing. The main issues of image restoration are deblurring, denoising and preserving fine details. In order to obtain good restored images, we propose a new image restoration method based on a compound regularization model associated with the weighted anisotropic total variation (WATV) and the tetrolets-based sparsity. The WATV recovers sharp edges by embedding two directional gradient operators into the original anisotropic total variation (ATV), and the tetrolet transform adapts its basis to the local image structures. Thus, our model can preserve details such as textures and edges in the processing of image restoration by combining the WATV with the tetrolets-based sparsity. We present an alternate iterative scheme which consists of the variable splitting method and the operator splitting method to solve the proposed minimization problem. Experimental results demonstrate the efficiency of our image restoration method for preserving the structure details and the sharp edges of image.