Constrained and SNR-Based Solutions for TV-Hilbert Space Image Denoising
Journal of Mathematical Imaging and Vision
A Truncated Lagrange Method for Total Variation-Based Image Restoration
Journal of Mathematical Imaging and Vision
An Algorithm for Image Denoising with Automatic Noise Estimate
Journal of Mathematical Imaging and Vision
Some First-Order Algorithms for Total Variation Based Image Restoration
Journal of Mathematical Imaging and Vision
A convex optimization approach for depth estimation under illumination variation
IEEE Transactions on Image Processing
SAR image regularization with fast approximate discrete minimization
IEEE Transactions on Image Processing
A total variation-based algorithm for pixel-level image fusion
IEEE Transactions on Image Processing
Joint depth-motion dense estimation for multiview video coding
Journal of Visual Communication and Image Representation
Dense disparity MAP representations for stereo image coding
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Total variation projection with first order schemes
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Journal of Mathematical Imaging and Vision
Adaptive kernel-based image denoising employing semi-parametric regularization
IEEE Transactions on Image Processing
Image processing techniques for assessing contractility in isolated adult cardiac myocytes
Journal of Biomedical Imaging
Incremental Subgradients for Constrained Convex Optimization: A Unified Framework and New Methods
SIAM Journal on Optimization
SIAM Journal on Scientific Computing
A primal-dual gradient method for image decomposition based on (BV, H-1)
Multidimensional Systems and Signal Processing
Image de-noising by Bayesian regression
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Combining iterative inverse filter with shock filter for baggage inspection image deblurring
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Journal of Mathematical Imaging and Vision
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Total variation has proven to be a valuable concept in connection with the recovery of images featuring piecewise smooth components. So far, however, it has been used exclusively as an objective to be minimized under constraints. In this paper, we propose an alternative formulation in which total variation is used as a constraint in a general convex programming framework. This approach places no limitation on the incorporation of additional constraints in the restoration process and the resulting optimization problem can be solved efficiently via block-iterative methods. Image denoising and deconvolution applications are demonstrated.