Computational Microscopy Using LED Array for 3D Phase Imaging - Prof. Lei Tian

Computational Microscopy Using LED Array for 3D Phase Imaging - Prof. Lei Tian

IEEE Signal Processing Society via YouTube Direct link

Compute SSNP forward model stepwise

29 of 33

29 of 33

Compute SSNP forward model stepwise

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Computational Microscopy Using LED Array for 3D Phase Imaging - Prof. Lei Tian

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  1. 1 Computational microscopy using an LED array
  2. 2 LED array microscope for multimodal computational imaging
  3. 3 Intensity diffraction tomography with controllable LED illumination
  4. 4 Linear single-scattering approximation model Sample
  5. 5 Single scattering information is directly visible in the Fourier space
  6. 6 3D reconstruction by slice-wise deconvolution
  7. 7 Scan-free 3D phase reconstruction on cell cluster
  8. 8 Expanding IDT's imaging limits
  9. 9 Multiplexed Intensity Diffraction Tomography (MIDT) for high volume-rate IDT
  10. 10 Multi-scale phase tomography
  11. 11 Annual IDT (alDT) further extends imaging limits
  12. 12 Real-time volumetric phase imaging
  13. 13 "Open" computational 3D phase microscopy
  14. 14 Single-scattering model limits IDT's reconstructio
  15. 15 Multiple-scattering model to improve accuracy
  16. 16 Nonlinear SSNP multiple-scattering model
  17. 17 Reconstruction by optimization
  18. 18 Reconstruction on multiple-scattering sample
  19. 19 Reconstruction on dynamic sample
  20. 20 Deep learning training leverages single-scattering! inversion and learns multiple-scattering physics
  21. 21 Deep learning training leverages single-scattering inversion and learns multiple-scattering physics
  22. 22 Network improves weak scattering recovery
  23. 23 Network generalizes to different optical setups
  24. 24 Network generalizes to dynamic multiple-scattering sami
  25. 25 Acknowledgements
  26. 26 Model and learning strategies for Computational 3D phase microscopy
  27. 27 Lightweight 2D U-Net for efficient 3D Recovery
  28. 28 Multiple-scattering model-based reconstruction is still limited
  29. 29 Compute SSNP forward model stepwise
  30. 30 Model-based data normalization for generalization across different contrasts
  31. 31 Network prediction validation
  32. 32 Multiple-scattering physical simulator for generating training data Neural network trained entirely on simulated data generated using multiple scattering models
  33. 33 Trained deep learning model generalizes to biological samples

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