The structure of optimal parameters for image restoration problems
By J. D. L. Reyes, Carola-Bibiane Schönlieb, Tuomo Valkonen. 2015
58 citations recorded by the source corpus.
In Cambridge, this project has been supported by King Abdullah University of Science and Technology (KAUST) Award No. KUK-I1-007-43, EPSRC grants Nr. EP/J009539/1 “Sparse & Higher-order Image Restoration”, and Nr. EP/M00483X/1 “Efficient computational tools for inverse imaging problems”. In Quito, the project has been supported by the Escuela Politecnica Nacional de Quito under award PIS 12-14 and the MATHAmSud project SOCDE “Sparse Optimal Control of Differential Equations”. When in Quito, T. Valkonen was moreover supported by a Prometeo scholarship of the Senescyt (Ecuadorian Ministry of Science, Technology, Education, and Innovation).
Subfields: Computational Mechanics, Mathematical Physics
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In Computational Mechanics
- Thomas Pock
- Martin Burger
- Martin Benning
- René Ranftl
- Thomas Brox
- Peter Ochs
- Fabian Pedregosa
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In Mathematical Physics
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Citations in the corpus
- Hyperparameter optimization with approximate gradient (2016, background): “At the same time recent progress on the setting of image restoration, which can be considered a subproblem of (HO), has given sufficient conditions on the input data for such solution to exist in an unbounded domain (De los Reyes et al., 2015).”
- Modern regularization methods for inverse problems (2018, methodology): “These cover the finitedimensional Markov random field models proposed in [325, 346, 143, 124, 334], the optimal model design approaches in [199, 198, 65, 40], the optimal regularization parameter estimation in variational regularization [89, 128, 137, 138, 90, 127], to training optimal operators in ”
- Techniques for Gradient-Based Bilevel Optimization with Non-smooth Lower Level Problems (2016, methodology): “A variational formulation for learning a good noise model was addressed in [34] in a PDE-constrained optimization framework, with some follow-up works [5,33,6].”