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Motion Deblurring Using Hybrid Imaging

Motion Deblurring Using Hybrid Imaging. Moshe Ben-Ezra and Shree K. Nayar Columbia University IEEE CVPR Conference June 2003, Madison, USA. Image Recording Requires Time . Daguerre 1829 1/2 hour exposure . Niépce 1827 8 hours exposure . Motion Blur is Everywhere. Object Motion.

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Motion Deblurring Using Hybrid Imaging

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  1. Motion Deblurring Using Hybrid Imaging Moshe Ben-Ezra and Shree K. Nayar Columbia University IEEE CVPR Conference June 2003, Madison, USA

  2. Image Recording Requires Time Daguerre1829 1/2 hour exposure Niépce 1827 8 hours exposure

  3. Motion Blur is Everywhere Object Motion Camera Motion

  4. Stabilized Lenses 1/15 second (< -5 stops) • Stabilization drifts with time • Rotation only 1/250 second (< -1 stop) Canon Stabilized lens 400mm

  5. Blind Image Deconvolution Accurate Point Spread Function (PSF) Needed.

  6. Motion Point Spread Function (PSF) Motion PSF is a Function of: Motion path Motion speed H Energy ~ 1/ speed Y X Spatial spread

  7. PSF Detector? PSF Detector Camera Can the PSF detector be a small and simple imaging device ?

  8. Fundamental Limits of Imaging Photon flux Detector Electron wells Pixel’s Signal Noise Detector’s noise level

  9. Fundamental Resolution Tradeoff Hybrid imaging system Hi-resolution camera Conventional video camera Low-resolution camera 3M 2048x1536 330K 720x480 75K 320x240 A Hybrid camera enjoys both worlds 3 Temporal resolution (fps) 30 130 Spatial resolution (pixels)

  10. Overview of Approach Motion Analysis y x PSF Estimation Deconvolution Low-Res. camera Same time period Hi-Res. camera

  11. Global Motion From Low Resolution Detector Objective function (Optical flow constraint) Rotation Translation Lucas Kanade

  12. Simulations: Motion Accuracy from Low- Res. Images Average Motion Error in Pixels

  13. Constraints on Continuous PSF Energy conservation constraint: • Constant flux assumption: Path is continuous and twice differentiable • Smoothness constraint:

  14. PSF Estimation from Computed Motion y y f6 f6 f1 f1 f5 f5 f2 f2 f3 f3 f4 f4 Frame 2 … Frame 5 x h4 h4 h5 h5 h3 h3 h2 h2 y h y h Frame 2 … Frame 5 Frame 2 … Frame 5 x x

  15. Deconvolution of High Resolution Image Standard iterative ratio-based algorithm* Image estimate PSF Error • Guaranties non-negative pixel result * Richardson [72] Lucy [74]

  16. Designs for Hybrid Imaging Using a beam splitter Using a special chip A rig of two cameras

  17. Our Prototype: Rig of Two Cameras Primary detector (2048x1536) Secondary detector (360x240) Resolution ratio of 1 : 36

  18. Example 1 - Blurred Hi-Res Image f = 633mm, Exp. Time 1 Sec (> -9 stops)

  19. PSF Estimation from Motion Estimated PSF 0.06 90 Y(Pixels) 10 0.001 X(Pixels) 10 130 Low resolution sequence. f = 633mm, Exp. Time 1 Sec

  20. Deblurred Image f = 633mm, Exp. Time 1 Sec

  21. Example 1 - Comparison Tripod image (Ground Truth) Deblurred image Blurred image f = 633mm, Exp. Time 1 Sec

  22. Example 2 - Blurred Night Image f = 884mm, Exp. Time 4 Sec (> -11 stops)

  23. PSF Estimation from Motion 0.003 30 Y(Pixels) 10 0.001 X(Pixels) 10 60 Low resolution sequence. f = 884mm, Exp. Time 4 Sec

  24. Deblurred Night Image f = 884mm, Exp. Time 4 Sec

  25. Example 3 - Comparison Tripod image (Ground Truth) Deblurred image Blurred image f = 884mm, Exp. Time 4 Sec

  26. Object Deblurring Problem Moving objects blend into the background

  27. Hybrid Imaging Solution (simulated) Requires clear high-resolution background image

  28. Thank you

  29. Quantifying The Affect of Motion Blur Empirical tests: RMS error. Volume of Solutions (Linear Model): Input Images High-Resolution Image Blur Decimation Uncertainty (Quantization) Volume of Solutions 1/det(A)

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