Skip to main content
SpringerLink
Log in

Iterative Feedback Estimation of Depth and Radiance from Defocused Images

  • Conference paper
Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7727))

Included in the following conference series:

Abstract

This paper presents a novel iterative feedback framework for simultaneous estimation of depth map and All-In-Focus (AIF) image, which benefits each other in each stage to obtain final convergence: For the recovery of AIF image, sparse prior of natural image is incorporated to ensure high quality defocus removal even under inaccurate depth estimation. In depth estimation step, we feed back the constraints from the high quality AIF image and adopt a numerical solution which is robust to the inaccuracy of AIF recovery to further raise the performance of DFD algorithm. Compared with traditional DFD methods, another advantage offered by this iterative framework is that by introducing AIF, which follows the prior knowledge of natural images to regularize the depth map estimation, DFD is much more robust to camera parameter changes. In addition, the proposed approach is a general framework that can incorporate depth estimation and AIF image recovery algorithms. The experimental results on both synthetic and real images demonstrate the effectiveness of the proposed method, especially on the challenging data sets containing large textureless regions and within a large range of camera parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Schechner, Y.Y., Kiryati, N.: Depth from defocus vs.stereo: How different really are they? International Journal of Computer Vision 39, 141–162 (2000)

    Article  MATH  Google Scholar 

  2. Krotkov, E.: Focusing. International Journal of Computer Vision 1, 223–237 (1987)

    Article  Google Scholar 

  3. Nayar, S.K., Nakagawa, Y.: Shape from focus. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 824–831 (1994)

    Article  Google Scholar 

  4. Shoji, H., Shirai, K., Ikehara, M.: Shape from focus using color segmentation and bilateral filter. In: Proceedings of 4th Signal Processing Education Workshop, pp. 566–571 (2006)

    Google Scholar 

  5. Chaudhuri, S., Rajagopalan, A.N.: Depth from defocus: a real aperture imaging approach. Springer (1999)

    Google Scholar 

  6. Favaro, P., Soatto, S.: 3D shape reconstruction and image restoration: exploiting defocus and motion blur. Springer (2006)

    Google Scholar 

  7. Levin, A., Fergus, R., Durand, F., Freeman, W.: Image and depth from a conventional camera with a coded aperture. ACM Transactions on Graphics (Proc. SIGGRAPH) 26, 70–78 (2007)

    Article  Google Scholar 

  8. Zhuo, S., Sim, T.: Recovering depth from a single defocused image (submitted to Pattern Recognition and online available)

    Google Scholar 

  9. Pentland, A.: A new sense for depth of field. IEEE Transactions on Pattern Analysis and Machine Intelligence 9, 523–531 (1987)

    Article  Google Scholar 

  10. Subbarao, M., Surya, G.: Depth from defocus: A spatial domain approach. International Journal of Computer Vision 13, 271–294 (1994)

    Article  Google Scholar 

  11. Favaro, P., Soatto, S.: Learning Shape from Defocus. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 735–745. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  12. Subbarao, M.: Parallel depth recovery by changing camera aperture. In: IEEE International Conference on Computer Vision, pp. 149–155 (1988)

    Google Scholar 

  13. Gokstorp, M.: Computing depth from out-of-focus blur using a local frequency representation. In: International Conference on Pattern Recognition, pp. 153–158 (1994)

    Google Scholar 

  14. Rajagopalan, A., Chaudhuri, S.: A block shift-variant blur model for recovering depth from defocused images. In: International Conference on Image Processing, pp. 636–639 (1995)

    Google Scholar 

  15. Watanabe, M., Nayar, S.: Rational filters for passive depth from defocus. International Journal of Computer Vision 27, 203–225 (1998)

    Article  Google Scholar 

  16. Rajagopalan, A., Chaudhuri, S.: An MRF model-based approach to simultaneous recovery of depth and restoration from defocused images. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 577 (1999)

    Article  Google Scholar 

  17. Namboodiri, V.P., Chaudhuri, S., Hadap, S.: Regularized depth from defocus. In: IEEE International Conference on Image Processing, pp. 1520–1523 (2008)

    Google Scholar 

  18. Favaro, P., Mennucci, A., Soatto, S.: Observing shape from defocused images. International Journal of Computer Vision 52, 25–43 (2003)

    Article  MATH  Google Scholar 

  19. Rajagopalan, A., Chaudhuri, S.: Space-variant approaches to recovery of depth from defocused images. Computer Vision and Image Understanding 68, 309–329 (1997)

    Article  Google Scholar 

  20. Favaro, P.: Recovering thin structures via nonlocal-means regularization with application to depth from defocus. In: International Conference on Computer Vision and Pattern Recognition, pp. 1133–1140 (2010)

    Google Scholar 

  21. Favaro, P., Soatto, S., Burger, M., Osher, S.: Shape from defocus via diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 518–531 (2008)

    Article  Google Scholar 

  22. Namboodiri, V., Chaudhuri, S.: On defocus, diffusion and depth estimation. Pattern Recognition Letters 28, 311–319 (2007)

    Article  Google Scholar 

  23. Cai, J., Ji, H., Liu, C., Shen, Z.: High-quality curvelet based motion deblurring from an image pair. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1566–1573 (2009)

    Google Scholar 

  24. Chen, J., Yuan, L., Tang, C., Quan, L.: Robust dual motion deblurring. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

    Google Scholar 

  25. Rav-Acha, A., Peleg, S.: Two motion-based images are better than one. Pattern Recognition Letters 26, 311–317 (2005)

    Article  Google Scholar 

  26. Tao, M., Yang, J.: Alternating direction algorithms for total variation deconvolution in image reconstruction. available at Optimization Online (2009)

    Google Scholar 

  27. Zitnick, C., Kang, S., Uyttendaele, M., Winder, S., Szeliski, R.: High-quality video view interpolation using a layered representation. ACM Transactions on Graphics (also Proc. SIGGRAPH) 23, 600–608 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Automation, Tsinghua University, China

    Xing Lin, Jinli Suo, Xun Cao & Qionghai Dai

Authors
  1. Xing Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinli Suo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xun Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qionghai Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 151-744, Seoul, Korea

    Kyoung Mu Lee

  2. Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China

    Yasuyuki Matsushita

  3. School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA

    James M. Rehg

  4. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District,, 100 190, Beijing, P.R. China

    Zhanyi Hu

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lin, X., Suo, J., Cao, X., Dai, Q. (2013). Iterative Feedback Estimation of Depth and Radiance from Defocused Images. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7727. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37447-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37447-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37446-3

  • Online ISBN: 978-3-642-37447-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation