1 / 40

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

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

lauril
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related