Learning Robust Imaging Models without Paired Data

Learning Robust Imaging Models without Paired Data

Society for Industrial and Applied Mathematics via YouTube Direct link

Visual results

32 of 33

32 of 33

Visual results

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Learning Robust Imaging Models without Paired Data

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  1. 1 Intro
  2. 2 Outline
  3. 3 Linear approximation for imaging process
  4. 4 The error effects
  5. 5 Model based approaches
  6. 6 Deep learning (DL) based approaches
  7. 7 Data bottleneck in DL
  8. 8 Data collection in video superresolution
  9. 9 Goal of the talk
  10. 10 Image denoising
  11. 11 The basic idea
  12. 12 Model formulation
  13. 13 Numerical method
  14. 14 One remark on overfitting issue
  15. 15 Quantitative results for real noisy images
  16. 16 Qualitative results
  17. 17 Latent space verification
  18. 18 Real-world noisy images from Huawei
  19. 19 Image segmentation
  20. 20 Probabilistic model
  21. 21 Examples
  22. 22 Deep CV model
  23. 23 Distributions in latent space
  24. 24 Motivation
  25. 25 The case of unpaired datasets
  26. 26 Unpaired degradation modeling
  27. 27 The idea
  28. 28 The loss function
  29. 29 Inference invariant condition
  30. 30 Synthetic noisy images
  31. 31 Experiments
  32. 32 Visual results
  33. 33 Summary

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