1 / 20

Image Restoration

School of Natural Sciences & Mathematics Department of Physics. Image Restoration. Juan Navarro Sorroche Phys-6314 Physics Department The University of Texas at Dallas Fall 2010. School of Natural Sciences & Mathematics Department of Physics. Image Restoration.

rgurrola
Download Presentation

Image Restoration

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. School of Natural Sciences & Mathematics Department of Physics Image Restoration Juan Navarro Sorroche Phys-6314 Physics Department The University of Texas at Dallas Fall 2010

  2. School of Natural Sciences & Mathematics Department of Physics Image Restoration • Motivations for image restoration • Pin-hole camera model • Sources of image distortion • Distortion models • Correcting algorithms and implementation

  3. School of Natural Sciences & Mathematics Department of Physics Introduction Image Distortion Motivations for image restoration 8’x4’ camera calibration board

  4. School of Natural Sciences & Mathematics Department of Physics Introduction Image Distortion Close up view of 8’x4’ camera calibration board

  5. School of Natural Sciences & Mathematics Department of Physics Introduction • Any DAQ system where images are created requires restoration of images • Oscilloscopes • Microscopes • X-rays machines • Robotic vision • CCD/CMOS sensors • Medical imaging equipment • Ionization chambers • Mass spectrometers • Any projective type of detector

  6. School of Natural Sciences & Mathematics Department of Physics Pin-Hole Camera Model Y X Z C Projective Transformation World coordinates to pixels transformation n, n0, m, m0 = # of pixels px, py = pixel size CCD/CMOS camera sensor pixel’s coordinates

  7. School of Natural Sciences & Mathematics Department of Physics Pin-Hole Camera Model Y X Z C Projective Transformation World coordinates to pixels transformation: general case For the case of camera rotation and translation K= Camera calibration matrix R=Rotation matrix C=camera center coordinates P=Projective Transformation matrix General expression for the camera transform xw,yw,zw homogenous 4-vector

  8. School of Natural Sciences & Mathematics Department of Physics Image Distortion Sources • Intrinsic • Radial distortion • Tangential distortion • Skew distortion • Extrinsic • Projection distortion • Perspective distortion • Skew distortion

  9. School of Natural Sciences & Mathematics Department of Physics Image Distortion Sources Perspective Distortion

  10. School of Natural Sciences & Mathematics Department of Physics Distortion Correction Models Distortion Models Radial distortion Radial functions • Commercial packages • Adobe • RoboRealm • PhotoModeller • FireWorks • Open Source • GIMP • Professional metrology • Halcon Model Most used Best approximation Easiest. Good approximation Perspective distortion

  11. School of Natural Sciences & Mathematics Department of Physics Correcting Algorithms & Implementation Calibration Board Pixel Plot

  12. School of Natural Sciences & Mathematics Department of Physics Correcting Algorithms & Implementation Radial Points - Fitting Function Plot

  13. School of Natural Sciences & Mathematics Department of Physics Correcting Algorithms & Implementation Pin-hole Vs. Radial Distortion Corrected Pixel Plot

  14. School of Natural Sciences & Mathematics Department of Physics Correcting Algorithms & Implementation Pin-hole Vs. Rad/Persp. Corrected Pixel Plot

  15. School of Natural Sciences & Mathematics Department of Physics Correcting Algorithms & Implementation > >

  16. School of Natural Sciences & Mathematics Department of Physics Correcting Algorithms & Implementation

  17. School of Natural Sciences & Mathematics Department of Physics Correcting Algorithms & Implementation

  18. School of Natural Sciences & Mathematics Department of Physics Correcting Algorithms & Implementation

  19. School of Natural Sciences & Mathematics Department of Physics Conclusions Conclusions Images must be corrected from optical system distortions prior of making any measurement Radial distortion affects object’s position determination & other derived variables Perspective distortion can leads to large errors in position determination depending on angle of tilt Distortions must be removed before ideal (pin-hole) camera transformations are made

More Related