Variational Models and Algorithms for GW Denoising and Reconstruction

Variational Models and Algorithms for GW Denoising and Reconstruction

Institute for Pure & Applied Mathematics (IPAM) via YouTube Direct link

Rudin-Osher-Fatemi model

6 of 18

6 of 18

Rudin-Osher-Fatemi model

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Variational Models and Algorithms for GW Denoising and Reconstruction

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  1. 1 Intro
  2. 2 GW signal detection
  3. 3 GW data analysis steps
  4. 4 Signal denoising approach
  5. 5 Introduction to TV methods
  6. 6 Rudin-Osher-Fatemi model
  7. 7 Split-Bregman method
  8. 8 Sparse representation of signals
  9. 9 The LASSO
  10. 10 Dictionary Learning problem
  11. 11 Search Optimal Regularization Parameter
  12. 12 Integration with CWB
  13. 13 Learning process
  14. 14 Dictionary learning results
  15. 15 CCSN mechanism extraction with LASSO
  16. 16 CCSN mechanism extraction with DL
  17. 17 lip denoising via dictionary learning
  18. 18 ummary and Conclusions

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