Image Enhancement Using Calibrated Lens Simulations

  • Yichang Shih
  • Brian Guenter
  • Neel Joshi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7575)


All lenses have optical aberrations which reduce image sharpness. These aberrations can be reduced by deconvolving an image using the lens point spread function (PSF). However, fully measuring a PSF is laborious and prohibitive. Alternatively, one can simulate the PSF if the lens model is known. However, due to manufacturing tolerances lenses differ subtly from their models, so often a simulated PSF is a poor match to measured data. We present an algorithm that uses a PSF measurement at a single depth to calibrate the nominal lens model to the measured PSF. The calibrated model can then be used to compute the PSF for any desired setting of lens parameters for any scene depth, without additional measurements or calibration. The calibrated model gives deconvolution results comparable to measurement but is much more compact and require hundreds of times fewer calibration images.


Point Spread Function Image Enhancement Chromatic Aberration Dispersion Function Lens Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
  2. 2.
    Banham, M.R., Katsaggelos, A.K.: Digital image restoration. IEEE Signal Processing Magazine 14(2), 24–41 (1997)CrossRefGoogle Scholar
  3. 3.
    Brauers, J., Seiler, C., Aach, T.: Direct psf estimation using a random noise target. In: Digital Photography, p. 75370 (2010)Google Scholar
  4. 4.
    Cannon, M.: Blind deconvolution of spatially invariant image blurs with phase. IEEE Transactions on Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing] 24(1), 58–63 (1976)CrossRefGoogle Scholar
  5. 5.
    Cathey, W.T., Dowski, E.R.: New paradigm for imaging systems. Appl. Opt. 41(29), 6080–6092 (2002)CrossRefGoogle Scholar
  6. 6.
    Conchello, J.-A., Lichtman, J.W.: Optical sectioning microscopy. Nature Methods 2(12), 920–931 (2005)CrossRefGoogle Scholar
  7. 7.
    Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graph. 25, 787–794 (2006)CrossRefGoogle Scholar
  8. 8.
    Goodman, J.W.: Introduction to Fourier Optics (2005)Google Scholar
  9. 9.
    Hanrahan, P., Ng, R.: Digital correction of lens aberrations in light field photography. In: International Optical Design, p. WB2. Optical Society of America (2006)Google Scholar
  10. 10.
    Hausler, G.: A method to increase the depth of focus by two step image processing. Optics Communications 6, 38–42 (1972)CrossRefGoogle Scholar
  11. 11.
    Joshi, N., Szeliski, R., Kriegman, D.J.: Psf estimation using sharp edge prediction. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8 (2008)Google Scholar
  12. 12.
    Kang, S.: Automatic removal of chromatic aberration from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)Google Scholar
  13. 13.
    Kee, E., Paris, S., Chen, S., Wang, J.: Modeling and removing spatially-varying optical blur. In: 2011 IEEE International Conference on Computational Photography (ICCP), pp. 1–8. IEEE (2011)Google Scholar
  14. 14.
    Krist, J.E.: Deconvolution of hubble space telescope images using simulated point spread functions. Astronomical Data Analysis Software and Systems (1992)Google Scholar
  15. 15.
    Levin, A.: Blind motion deblurring using image statistics. In: NIPS, pp. 841–848 (2006)Google Scholar
  16. 16.
    Levin, A., Sand, P., Cho, T.S., Durand, F., Freeman, W.T.: Motion-invariant photography. ACM Trans. Graph. 27, 71:1–71:9 (2008)Google Scholar
  17. 17.
    McGuire Jr., J., et al.: Designing easily manufactured lenses using a global method. In: International Optical Design Conference. Optical Society of America (2006)Google Scholar
  18. 18.
    Meiron, J.: Damped least-squares method for automatic lens design. JOSA 55(9), 1105–1107 (1965)CrossRefGoogle Scholar
  19. 19.
    Nayar, S.K., Watanabe, M., Noguchi, M.: Real-time focus range sensor. IEEE Trans. Pattern Anal. Mach. Intell. 18, 1186–1198 (1996)CrossRefGoogle Scholar
  20. 20.
    Scalettar, B., Swedlow, J., Sedat, J., Agard, D.: Dispersion, aberration and deconvolution in multi-wavelength fluorescence images. Journal of Microscopy 182(1), 50–60 (1996)CrossRefGoogle Scholar
  21. 21.
    Xu, L., Jia, J.: Two-Phase Kernel Estimation for Robust Motion Deblurring. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 157–170. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. 22.
    Zhou, C., Lin, S., Nayar, S.: Coded aperture pairs for depth from defocus. In: ICCV, Kyoto, Japan (October 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yichang Shih
    • 1
    • 2
  • Brian Guenter
    • 1
  • Neel Joshi
    • 1
  1. 1.Microsoft ResearchUSA

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