The Magic of Intelligent Coherent Optical Processing

By Charles Addison Bouman

Electrical and Computer Engineering, Purdue University, West Lafayette, IN

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Bio

Charles A. Bouman Charles A. Bouman received a B.S.E.E. degree from the University of Pennsylvania in 1981 and a MS degree from the University of California at Berkeley in 1982. From 1982 to 1985, he was a full staff member at MIT Lincoln Laboratory and in 1989 he received a Ph.D. in electrical engineering from Princeton University. He joined the faculty of Purdue University in 1989 where he is currently the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering.

Prof. Bouman’s research is in the area of computational imaging and sensing where he is focused on the integration of signal processing, statistical modeling, physics, and computation to solve difficult sensing problems with applications in healthcare, material science, physics, chemistry, and commercial imaging. His research resulted in the first commercial model-based iterative reconstruction (MBIR) system for medical X-ray computed tomography (CT), and he is co-inventor on over 50 issued patents that have been licensed and used in millions of consumer imaging products. Professor Bouman is a member of the National Academy of Inventors, a Fellow of the IEEE, a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), a Fellow of the society for Imaging Science and Technology (IS&T), a Fellow of the SPIE professional society. He is the recipient of the 2014 Electronic Imaging Scientist of the Year award, and the IS&T’s Raymond C. Bowman Award. He has been a Purdue University Faculty Scholar and received the College of Engineering Engagement/Service Award, and Team Award. He was also founding co-director of Purdue’s Magnetic Resonance Imaging Facility from 2007-2016, and led of Purdue’s Integrated Imaging Cluster from 2012-2016. He was previously the Editor-in-Chief for the IEEE Transactions on Image Processing; a Distinguished Lecturer for the IEEE Signal Processing Society; and a Vice President of Technical Activities for the IEEE Signal Processing Society, during which time he led the creation of the IEEE Transactions on Computational Imaging. He has been an associate editor for the IEEE Transactions on Image Processing, the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the SIAM Journal on Mathematical Imaging. He has also been a Vice President of Publications and a member of the Board of Directors for the IS&T Society, and he is the founder and Co-Chair of the SPIE/IS&T conference on Computational Imaging.

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Researchers should cite this work as follows:

  • Charles Addison Bouman (2020), "The Magic of Intelligent Coherent Optical Processing," https://nanohub.org/resources/32867.

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Hall for Discovery and Learning Research, Purdue University, West Lafayette, IN

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The Magic of Intelligent Coherent Optical Processing
  • The Magic of Intelligent Coherent Optcal Processing 1. The Magic of Intelligent Coher… 0
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  • Outline 2. Outline 36.302969636302969
    00:00/00:00
  • What is Computational Imaging? (Integrated Imaging) 3. What is Computational Imaging?… 159.79312645979314
    00:00/00:00
  • Model Based Iterative Reconstruction (MBIR) 4. Model Based Iterative Reconstr… 253.42008675342009
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  • 5. "Thin Manifold" View of Prior … 338.87220553887221
    00:00/00:00
  • Phase Recovery for Complex Signals 6. Phase Recovery for Complex Sig… 455.38872205538871
    00:00/00:00
  • Phase Recovery with Heterodyne Demodulation 7. Phase Recovery with Heterodyne… 498.76543209876547
    00:00/00:00
  • Digital Holography: Math versus Experiment 8. Digital Holography: Math versu… 575.709042375709
    00:00/00:00
  • Digital Holography: Graphical 9. Digital Holography: Graphical 630.99766433099774
    00:00/00:00
  • General Phase Recovery 10. General Phase Recovery 690.32365699032368
    00:00/00:00
  • Example: Phase Recovery with Aliasing 11. Example: Phase Recovery with A… 885.71905238571912
    00:00/00:00
  • Example: Phase Recovery with Aliasing 12. Example: Phase Recovery with A… 955.42208875542212
    00:00/00:00
  • Example: Ptychography 13. Example: Ptychography 1002.0020020020021
    00:00/00:00
  • Ptychographic Reconstruction 14. Ptychographic Reconstruction 1087.0203536870204
    00:00/00:00
  • Imaging through Turbulence 15. Imaging through Turbulence 1122.5558892225558
    00:00/00:00
  • Variables to remember… 16. Variables to remember… 1239.3393393393394
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  • Conventional Estimation of ! 17. Conventional Estimation of ! 1305.8391725058393
    00:00/00:00
  • Huge Advantage of Estimating ! 18. Huge Advantage of Estimating ! 1350.0166833500168
    00:00/00:00
  • MBIR (Model-Based Iterative Reconstruction) 19. MBIR (Model-Based Iterative Re… 1398.5652318985653
    00:00/00:00
  • The Magic: EM Algorithm to the Rescue 20. The Magic: EM Algorithm to the… 1469.6029362696029
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  • Iterative EM Optimization 21. Iterative EM Optimization 1531.5315315315315
    00:00/00:00
  • Magical Closed Form for I Function! 22. Magical Closed Form for I Func… 1579.2459125792459
    00:00/00:00
  • Isoplanatic Experiments 23. Isoplanatic Experiments 1615.9826493159826
    00:00/00:00
  • DH-MBIR: Isoplanatic Result (Experimental) 24. DH-MBIR: Isoplanatic Result (E… 1642.9429429429431
    00:00/00:00
  • DH-MBIR: Strehl Ratio vs SNR (Simulated) 25. DH-MBIR: Strehl Ratio vs SNR (… 1697.7644310977646
    00:00/00:00
  • Anisoplanatic Experiments 26. Anisoplanatic Experiments 1719.3193193193194
    00:00/00:00
  • DH-MBIR: Anisoplanatic Results (Simulated) 27. DH-MBIR: Anisoplanatic Results… 1720.7207207207207
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  • Plug and Play/Consensus Equilibrium Approach 28. Plug and Play/Consensus Equili… 1725.7590924257593
    00:00/00:00
  • PnP Reconstruction (Simulation Data) 29. PnP Reconstruction (Simulation… 1759.3927260593928
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  • Takeaways… 30. Takeaways… 1776.4764764764766
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