High-Order Total Variation-Based Image Restoration
SIAM Journal on Scientific Computing
Convex Optimization
Photometric Stereo with General, Unknown Lighting
International Journal of Computer Vision
Image restoration combining a total variational filter and a fourth-order filter
Journal of Visual Communication and Image Representation
Interactive normal reconstruction from a single image
ACM SIGGRAPH Asia 2008 papers
An Improved LOT Model for Image Restoration
Journal of Mathematical Imaging and Vision
Image Denoising Using TV-Stokes Equation with an Orientation-Matching Minimization
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Image Recovery via Nonlocal Operators
Journal of Scientific Computing
A TV-stokes denoising algorithm
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Fast image recovery using variable splitting and constrained optimization
IEEE Transactions on Image Processing
Restoration of images based on subspace optimization accelerating augmented Lagrangian approach
Journal of Computational and Applied Mathematics
A Unified Primal-Dual Algorithm Framework Based on Bregman Iteration
Journal of Scientific Computing
SIAM Journal on Imaging Sciences
Orientation-Matching Minimization for Image Denoising and Inpainting
International Journal of Computer Vision
SIAM Journal on Imaging Sciences
A Modified TV-Stokes Model for Image Processing
SIAM Journal on Scientific Computing
Augmented Lagrangian Method for Generalized TV-Stokes Model
Journal of Scientific Computing
A Fast Fixed Point Algorithm for Total Variation Deblurring and Segmentation
Journal of Mathematical Imaging and Vision
IEEE Transactions on Image Processing
Noise removal using smoothed normals and surface fitting
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing
Kernel Regression for Image Processing and Reconstruction
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Deblurring Using Regularized Locally Adaptive Kernel Regression
IEEE Transactions on Image Processing
Journal of Computational and Applied Mathematics
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Recently, TV-Stokes model has been widely researched for various image processing tasks such as denoising and inpainting. In this paper, we introduce a new TV-Stokes model for image deconvolution, and propose fast and efficient iterative algorithms based on the augmented Lagrangian method. The new TV-Stokes model is a two-step model: in the first step, a smoothed and divergence free tangential field of the observed image is recovered based on total variation (TV) minimization and a new data fidelity term; in the second step, the image is reconstructed by minimizing the distance between the unit image gradient and the regularized unit normal direction. Numerical experiments demonstrate that the proposed model has the potential to outperform popular TV-based restoration methods in preserving texture details and fine structures. As a result, an improvement in signal-to-noise ratio (SNR) is obtained for deconvolution and denoising results.