An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Adapted Total Variation for Artifact Free Decompression of JPEG Images
Journal of Mathematical Imaging and Vision
Structure-Texture Image Decomposition--Modeling, Algorithms, and Parameter Selection
International Journal of Computer Vision
Constrained and SNR-Based Solutions for TV-Hilbert Space Image Denoising
Journal of Mathematical Imaging and Vision
Graph regularization for color image processing
Computer Vision and Image Understanding
A Truncated Lagrange Method for Total Variation-Based Image Restoration
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision
Incremental Subgradients for Constrained Convex Optimization: A Unified Framework and New Methods
SIAM Journal on Optimization
Approximate methods for constrained total variation minimization
CSR'06 Proceedings of the First international computer science conference on Theory and Applications
Constrained total variation minimization and application in computerized tomography
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A linearly convergent first-order algorithm for total variation minimisation in image processing
International Journal of Bioinformatics Research and Applications
An infeasible-point subgradient method using adaptive approximate projections
Computational Optimization and Applications
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The formulation of a wide variety of image recovery problems leads to the minimization of a convex objective over a convex set representing the constraints derived from a priori knowledge and consistency with the observed signals. In previous years, nondifferentiable objectives have become popular due in part to their ability to capture certain features such as sharp edges. They also arise naturally in minimax inconsistent set theoretic recovery problems. At the same time, the issue of developing reliable numerical algorithms to solve such convex programs in the context of image recovery applications has received little attention. We address this issue and propose an adaptive level set method for nondifferentiable constrained image recovery. The asymptotic properties of the method are analyzed and its implementation is discussed. Numerical experiments illustrate applications to total variation and minimax set theoretic image restoration and denoising problems.