1 / 19

Motion Compensated SNR and DR Enhancement With Motion Blur Prevention Using Multicapture

Motion Compensated SNR and DR Enhancement With Motion Blur Prevention Using Multicapture. Ali Ercan & Ulrich Barnhoefer. Introduction & Motivation. Single exposure trade-off High noise if short exposure time Motion blur if long exposure time. Introduction & Motivation. DR is another problem

ulmer
Download Presentation

Motion Compensated SNR and DR Enhancement With Motion Blur Prevention Using Multicapture

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. Motion Compensated SNR and DR Enhancement With Motion Blur Prevention Using Multicapture Ali Ercan & Ulrich Barnhoefer

  2. Introduction & Motivation • Single exposure trade-off • High noise if short exposure time • Motion blur if long exposure time Ali Ercan & Ulrich Barnhoefer

  3. Introduction & Motivation • DR is another problem • Short exposure: Dark areas in the scene cannot be seen • Long exposure: Bright areas saturate • If both high DR scene and motion, with single capture • Motion blur free, but noisy and non-visible dark areas image • Less noisy, but motion blurred and saturated image Ali Ercan & Ulrich Barnhoefer

  4. Introduction & Motivation • Our approach to solve these problems: • Use of multicapture combined with motion estimation • High speed is definitely needed • Normal video mode can be used – poorer results due to noise adding • CMOS imagers suitable • For a better understanding, let us introduce a simple model of CMOS imagers and describe multicapture Ali Ercan & Ulrich Barnhoefer

  5. Sensor Model • Charge Integration • Light on photodiode generates charges • Saturation when well capacity is reached • Noise sources • (Reset noise) • Shot noise UT • Read noise VT,Vo • (Dark current) • (Fixed pattern noise) Ali Ercan & Ulrich Barnhoefer

  6. Multicapture • Nondestructive multiple readout – Single integration • Less noise per capture compared to conventional video sensor – readout noise Ali Ercan & Ulrich Barnhoefer

  7. PHOTO-CURRENT ESTIMATOR CAMERA SIMULATOR SCENE MOTION ESTIMATOR FINAL IMAGE Implemented Algorithm Ali Ercan & Ulrich Barnhoefer

  8. Camera Simulator • Multicapture, noise, ADC implemented – pixel values out Ali Ercan & Ulrich Barnhoefer

  9. Motion Estimator • Block based motion estimation on difference frames • Search range ±1 and block size 3x3 • Fast imager (e.g. 10,000 fps available) • Search range and block size can be increased in expense of computational load • Noise suppression • Known noise levels – characterized CMOS sensor • Error = SSD + xDistance •  is proportional to noise • Thanks to Sebe! Ali Ercan & Ulrich Barnhoefer

  10. Motion Estimator • Estimated and perfect motion vectors Ali Ercan & Ulrich Barnhoefer

  11. A B C Photocurrent Estimator Ali Ercan & Ulrich Barnhoefer

  12. Photocurrent Estimator Ali Ercan & Ulrich Barnhoefer

  13. Results Ali Ercan & Ulrich Barnhoefer

  14. Results Ali Ercan & Ulrich Barnhoefer

  15. Results Ali Ercan & Ulrich Barnhoefer

  16. IMAGE ERRORS (STD OF ERROR IMAGE) CHECKER LENA CAMERAMAN 10 ms image 100.9 69.43 71.31 160 ms image 70.79 33.84 37.41 Const. with est. motion vectors 2.587 21.28 12.05 Const. with perfect motion vectors 2.576 17.22 3.092 Results Ali Ercan & Ulrich Barnhoefer

  17. Conclusion • Promising results achieved with this preliminary analysis • Motion blur reduced • Noise reduced • DR increased in dark end and in bright end in special cases Ali Ercan & Ulrich Barnhoefer

  18. Conclusion • Lots of more things to do • Use more sophisticated motion estimation algorithms • Separate motion detection from motion estimation and do estimation when detection occurs • Include extension of DR with sensor saturation • Handle the occlusions Ali Ercan & Ulrich Barnhoefer

  19. Questions Ali Ercan & Ulrich Barnhoefer

More Related