Harnessing defocus blur to recover high-resolution information in shape-from-focus technique

Access Full Text

Harnessing defocus blur to recover high-resolution information in shape-from-focus technique

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Published

Traditional shape-from-focus (SFF) uses focus as the singular cue to derive the shape profile of a 3D object from a sequence of images. However, the stack of low-resolution (LR) observations is space-variantly blurred because of the finite depth of field of the camera. The authors propose to exploit the defocus information in the stack of LR images to obtain a super-resolved image as well as a high-resolution (HR) depth map of the underlying 3D object. Appropriate observation models are used to describe the image formation process in SFF. Local spatial dependencies of the intensities of pixels and their depth values are accounted for by modelling the HR image and the HR structure as independent Markov random fields. Taking as input the LR images from the stack and the LR depth map, the authors first obtain the super-resolved image of the 3D specimen and use it subsequently to reconstruct a HR depth profile of the object.

Inspec keywords: image resolution; Markov processes; image sequences; image reconstruction; random processes

Other keywords: Markov random field; shape-from-focus technique; camera; super-resolved image; 3D specimen; low-resolution observation; information defocus; image sequences; image reconstruction

Subjects: Markov processes; Computer vision and image processing techniques; Optical, image and video signal processing; Markov processes

References

    1. 1)
      • Wang, O., Finger, J., Yang, Q., Davis, J., Yang, R.: `Automatic natural video matting with depth', Proc. 15th Pacific Conf. Computer Graphics and Applications, 2007, Maui, Hawaii, p. 469–472.
    2. 2)
      • Diebel, J., Thrun, S.: `An application of MRFs to range sensing', Proc. Neural Information Processing Systems Conf., 2005, Vancouver, Canada, p. 291–298.
    3. 3)
      • Subbarao, M.: `Parallel depth recovery by changing camera parameters', Proc. IEEE Int. Conf. Computer Vision, 1988, FL, USA, p. 149–155.
    4. 4)
      • D. Marr , T. Poggio . Cooperative computation of stereo disparity. Science , 4262 , 283 - 287
    5. 5)
      • S.K. Nayar , Y. Nakagawa . Shape from focus. IEEE Trans. Pattern Anal. Mach. Intell. , 8 , 824 - 831
    6. 6)
      • S.Z. Li . (1995) Markov random field modeling in computer vision.
    7. 7)
      • N.K. Bose , M.K. Ng , A.C. Yau . A fast algorithm for image super-resolution from blurred observations. EURASIP J. Appl. Signal Process.
    8. 8)
      • Woodham, R.J.: `A cooperative algorithm for determining surface orientation from a single view', Proc. Int. Joint Conf. Artificial Intelligence, 1977, Cambridge, MA, p. 635–641.
    9. 9)
      • S. Baker , T. Kanade . Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell. , 9 , 1167 - 1183
    10. 10)
      • Yang, Q., Yang, R., Davis, J., Nister, D.: `Spatial depth super resolution for range images', Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007, Minneapolis, USA, p. 1–8.
    11. 11)
      • D. Rajan , S. Chaudhuri . An MRF-based approach to generation of super-resolution images from blurred observations. J. Math. Imaging Vis. , 1 , 5 - 15
    12. 12)
      • D. Rajan , S. Chaudhuri . Simultaneous estimation of super-resolved scene and depth map from low resolution defocused observations. IEEE Trans. Pattern Anal. Mach. Intell. , 9 , 1102 - 1117
    13. 13)
      • A. Papoulis . Generalized sampling expansion. IEEE Trans. Circuits Syst. , 11 , 652 - 654
    14. 14)
      • T. Jebara , A. Azarbayejani , A. Pentland . 3D structure from 2D motion. IEEE Signal Process. Mag. , 3 , 66 - 84
    15. 15)
      • M. Joshi , S. Chaudhuri . Simultaneous estimation of super-resolved depth map and intensity field using photometric cue. Computer Vis. Image Underst. , 1 , 31 - 44
    16. 16)
      • J.J. Gibson . (1950) The perception of the visual world.
    17. 17)
      • J.E. Besag . Spatial interaction and the statistical analysis of lattice systems. J. R. Stat. Soc. B , 2 , 192 - 236
    18. 18)
      • P. Kauff , N. Atzpadin , C. Fehn . Depth map creation and image-based rendering for advanced 3DTV services providing interoperability and scalability. Signal Process., Image Commun. , 2 , 217 - 234
    19. 19)
      • A.P. Pentland . A new sense for depth of field. IEEE Trans. Pattern Anal. Mach. Intell. , 4 , 523 - 531
    20. 20)
      • S. Farsiu , M.D. Robinson , M. Elad , P. Milanfar . Fast and robust multiframe super resolution. IEEE Trans. Image Process. , 10 , 1327 - 1344
    21. 21)
      • Fehn, C.: `A 3D-TV approach using depth-image-based rendering (DIBR)', Proc. 3rd IASTED Conf. Visualization, Imaging and Image Processing, 2003, Spain, p. 482–487.
    22. 22)
      • M. Elad , Y. Hel-Or . A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur. IEEE Trans. Image Process. , 8 , 1187 - 1193
    23. 23)
      • J.C. Carr , W.R. Fright , R.K. Beatson . Surface interpolation with radial basis functions for medical imaging. IEEE Trans. Med. Imaging , 1 , 96 - 107
    24. 24)
      • Hertzmann, A., Jacobs, C., Oliver, N., Curless, B., Salesin, D.: `Image analogies', Proc. ACM SIGGRAPH, 2001, Los Angeles, USA, p. 327–340.
    25. 25)
      • Kil, Y.J., Mederos, B., Amenta, N.: `Laser scanner super-resolution', Proc. Eurographics Symp. Point-based Graphics, 2006, Boston, USA, p. 9–16.
    26. 26)
      • B.K.P. Horn , P.H. Winston . (1975) Obtaining shape from shading information, The psychology of computer vision.
    27. 27)
      • S. Chaudhuri , A.N. Rajagopalan . (1999) Depth from defocus: a real aperture imaging approach.
    28. 28)
      • W. Freeman , E. Pasztor , O. Carmichael . Learning low-level vision. Int. J. Comput. Vis. , 1 , 25 - 47
    29. 29)
      • M. Elad , A. Feuer . Restoration of a single superresolution image from several blurred, noisy, and under-sampled measured images. IEEE Trans. Image Process. , 12 , 1646 - 1658
    30. 30)
      • R.K. Bajcsy , L.I. Lieberman . Texture gradient as a depth cue. Comput. Graph. Image Process. , 1 , 52 - 67
    31. 31)
      • O.Y. Boykov , R. Zabih . Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. , 11 , 1222 - 1239
    32. 32)
      • P. Haigron , M.E. Bellemare , O. Acosta . Depth-map-based scene analysis for active navigation in virtual angioscopy. IEEE Trans. Med. Imaging , 11 , 1380 - 1390
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi_20070072
Loading

Related content

content/journals/10.1049/iet-cvi_20070072
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading