COAL: a generic modelling and prototyping framework for convex optimization problems of variational image analysis

DOI10.1080/10556788.2012.672571zbMath1278.90307OpenAlexW1964417189MaRDI QIDQ2867425

Jan Lellmann, Dirk Breitenreicher, Christoph Schnörr

Publication date: 19 December 2013

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556788.2012.672571

zbMATH Keywords

convex optimizationimage processingvariational modellingsparse large-scale programming

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Convex programming (90C25) Computing methodologies for image processing (68U10) Machine vision and scene understanding (68T45)

Related Items (2)

Global minimization for continuous multiphase partitioning problems using a dual approach ⋮ Algorithm design through the optimization of reuse-based generation

Uses Software

• AMPL
• FLENS
• CVX
• Mosek
• COAL

Cites Work

• Smooth minimization of non-smooth functions
• Templates for convex cone problems with applications to sparse signal recovery
• A first-order primal-dual algorithm for convex problems with applications to imaging
• A framelet-based image inpainting algorithm
• Lectures on Modern Convex Optimization
• Graphical Models, Exponential Families, and Variational Inference
• Handbook of Mathematical Models in Computer Vision
• Signal Recovery by Proximal Forward-Backward Splitting

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