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sroubekf@utias.cz Institute of Information Theo r y and Automation

Explore the world of superresolution imaging, covering Nyquist sampling, sub-Nyquist sampling, multichannel blind deconvolution, resolution enhancement, and more. Learn about realistic superresolution techniques, acquisition models, optimization methods, space-variant cases, camera-motion blur, and infrared video super-resolution. Discover how superresolution can be applied to handheld IR cameras, smartphones using Wiener filtering, and collaboration with esteemed researchers. Join us in advancing the field of imaging technology!

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sroubekf@utias.cz Institute of Information Theo r y and Automation

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  1. Superresolution Imaging from Equations to Mobile Applications Filip Šroubek sroubekf@utia.cas.cz Institute of Information Theory and Automation Academy of Sciences of the Czech Republic www.utia.cas.cz NIPS Workshop 2011 Sierra Nevada

  2. Superresolution

  3. Recalling Nyquist

  4. Nyquist Sampling • Nyquist sampling  an "ideal" image, no frequencies are missed. • Superresolution = scaling + interpolation

  5. Superresolution • Sub-Nyquist sampling  aliasing  high frequencies are lost or modified • Superresolution can recover aliased data interpolation SR

  6. Traditional superresolutionsub-Nyquistsampling

  7. pixel interpolation  superresolution Traditional superresolution sub-pixel shift

  8. Realistic Superresolution Deconvolution

  9. noise channel K channel 2 + channel 1 acquired images D[u* hk](x) + nk(x) = zk(x) Multichannel Acquisition Model original image u(x)

  10. Realistic superresolution • Acquisition model: • Noise • Blur • Shift • Downsampling • Reconstruction method: • Multichannel blind deconvolution • Shift compensation • Resolution enhancement

  11. Image regularization term L1 norm Blur Regularization term positivity Data term L2 norm Superresolution & Blind Deconv. • Acquisition model • Optimization problem

  12. Alternating Minimization • u-step • h-step

  13. Variable Splitting • u-step • h-step

  14. Augmented Lagrangian Method • u-step • h-step

  15. rough registration Optical zoom (ground truth) Superresolved image (2x) Superresolution SIAM Imaging Science 2010

  16. Space-variant Case

  17. Space-variant Case • Video with local motion • Masking • Slowly changing PSFs and/or misregistered images • Patch-wise approach

  18. interpolated SR

  19. interpolated SR t-2 t-1 t t+1 t+2

  20. interpolated SR SR + masking t-2 t-1 t t+1 t+2

  21. Camera-motion Blur

  22. Space-variant Superresolution

  23. Close-up Original Input LR Space-invariant Reconstruction Space-variant Reconstruction

  24. Infrared video super-resolution • Handheld IR camera • 160 x 120, 9 fps • Real-time super-resolution • with factor 2 (320 x 240) • computed directly inside the camera using DSP

  25. LR input SR output

  26. “Blind” Deconvolution on Smartphones Using accelerometers and/or gyroscopes Rotation and translation of the phone

  27. Wiener Filtering on Android Smartphones

  28. Wiener Filtering on Android Smartphones

  29. Thank You… collaborators: Jan Flusser, Michal Šorel, Jan Kamenický, Peyman Milanfar

  30. Regularization Terms

  31. z1 = u h1 h2 u = z2 * * z1 h2 = u h1 h2 h1 h2 u = h1 z2 * * * * * * 0 PSF Regularization u

  32. Incorporating a between-image shift original image PSF degraded image

  33. Wiener Filtering in Android Smartphones

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