Bilevel Learning Approaches in Variational Image

Bilevel Learning Approaches in Variational Image

Hausdorff Center for Mathematics via YouTube Direct link

Learning TV denoising model

14 of 32

14 of 32

Learning TV denoising model

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Bilevel Learning Approaches in Variational Image

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  1. 1 Intro
  2. 2 Outline
  3. 3 A generic inverse problem in imaging
  4. 4 The variational approach..
  5. 5 Modelling
  6. 6 Total variation (TV) denoising Least squares minimization
  7. 7 Modified non-local means Giboa Osher (2007)
  8. 8 State of the art in optimal model design
  9. 9 Bilevel optimal reconstruction model Assumptions
  10. 10 Learning from training sets
  11. 11 Learning by optimisation in imaging
  12. 12 Learning in function space
  13. 13 A generic TV denoising model
  14. 14 Learning TV denoising model
  15. 15 State of the art on optimality systems
  16. 16 In this setting we can prove
  17. 17 Optimality system for the regularized problems
  18. 18 Optimality system for bilevel problem
  19. 19 Numerical notes
  20. 20 Mixed Gauss & Poisson noise
  21. 21 Impulse noise
  22. 22 Partial conclusions
  23. 23 Nonhomogeneous noise
  24. 24 Ingredients for optimality conditions
  25. 25 Experiments
  26. 26 Motivation
  27. 27 Forward denoising problem
  28. 28 The kernel
  29. 29 Different kernels, different results
  30. 30 Bilevel optimization problem Optimal weight
  31. 31 What we are trying to do..
  32. 32 Conclusions and outlook

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