Sub-pixel Layout for Super-Resolution with Images in the Octic Group

  • Boxin Shi
  • Hang Zhao
  • Moshe Ben-Ezra
  • Sai-Kit Yeung
  • Christy Fernandez-Cull
  • R. Hamilton Shepard
  • Christopher Barsi
  • Ramesh Raskar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8689)

Abstract

This paper presents a novel super-resolution framework by exploring the properties of non-conventional pixel layouts and shapes. We show that recording multiple images, transformed in the octic group, with a sensor of asymmetric sub-pixel layout increases the spatial sampling compared to a conventional sensor with a rectilinear grid of pixels and hence increases the image resolution. We further prove a theoretical bound for achieving well-posed super-resolution with a designated magnification factor w.r.t. the number and distribution of sub-pixels. We also propose strategies for selecting good sub-pixel layouts and effective super-resolution algorithms for our setup. The experimental results validate the proposed theory and solution, which have the potential to guide the future CCD layout design with super-resolution functionality.

Keywords

Super-resolution CCD sensor Sub-pixel layout Octic group 

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References

  1. 1.
    Baker, S., Kanade, T.: Limits on super-resolution and how to break them. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9), 1167–1183 (2002)CrossRefGoogle Scholar
  2. 2.
    Ben-Ezra, M., Lin, Z., Wilburn, B., Zhang, W.: Penrose pixels for super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(7), 1370–1383 (2011)CrossRefGoogle Scholar
  3. 3.
    Ben-Ezra, M., Zomet, A., Nayar, S.K.: Video super-resolution using controlled subpixel detector shifts. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 977–987 (2005)CrossRefGoogle Scholar
  4. 4.
    Burke, B.E., Tonry, J., Cooper, M., Luppino, G., Jacoby, G., Bredthauer, R., Boggs, K., Lesser, M., Onaka, P., Young, D., Doherty, P., Craig, D.: The orthogonal-transfer array: A new CCD architecture for astronomy. In: Proceedings of the SPIE, Optical and Infrared Detectors for Astronomy, vol. 5499, pp. 185–192 (2004)Google Scholar
  5. 5.
    Candes, E., Romberg, J.: ℓ1-magic: Recovery of sparse signals via convex programming (2005), http://users.ece.gatech.edu/~justin/l1magic/
  6. 6.
    Elad, M., Feuer, A.: Restoration of single super-resolution image from several blurred, noisy and down-sampled measured images. IEEE Transactions on Image Processing 6(12), 1646–1658 (1997)CrossRefGoogle Scholar
  7. 7.
    Evangelidis, G.D., Psarakis, E.Z.: Parametric image alignment using enhanced correlation coefficient maximization. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(10), 1858–1865 (2008)CrossRefGoogle Scholar
  8. 8.
    Farsiu, S., Elad, M., Milanfar, P.: Multiframe demosaicing and super-resolution of color images. IEEE Transactions on Image Processing 15(1), 141–159 (2006)CrossRefGoogle Scholar
  9. 9.
    Freeman, W.T., Pasztor, E.C.: Learning low-level vision. In: Proc. of International Conference on Computer Vision (ICCV), pp. 1182–1189 (1999)Google Scholar
  10. 10.
  11. 11.
    Irani, M., Peleg, S.: Improving resolution by image restoration. Computer Vision, Graphics, and Image Processing 53, 231–239 (1991)Google Scholar
  12. 12.
    Lin, Z., Shum, H.Y.: Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1), 83–97 (2004)CrossRefGoogle Scholar
  13. 13.
    Moreno, I., Paez, G., Strojnik, M.: Dove prism with increased throughput for implementation in a rotational-shearing interferometer. Applied Optics 42(22), 4514–4521 (2003)CrossRefGoogle Scholar
  14. 14.
    Murty, M.V.R.K., Hagerott, E.C.: Rotational shearing interferometry. Applied Optics 5(4), 615–619 (1966)CrossRefGoogle Scholar
  15. 15.
    Paige, C.C., Saunders, M.A.: LSQR: An algorithm for sparse linear equations and sparse least squares. ACM Transactions on Mathematical Software 8(1), 43–71 (1982)CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: A technical overview. IEEE Signal Processing Magazine 20(3), 21–36 (2003)CrossRefGoogle Scholar
  17. 17.
    Sasao, T., Hiura, S., Sato, K.: Super-resolution with randomly shaped pixels and sparse regularization. In: Proc. of International Conference on Computational Photography (ICCP), pp. 1–11 (2013)Google Scholar
  18. 18.
    Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Transactions on Image Processing 19(11), 2861–2873 (2008)CrossRefMathSciNetGoogle Scholar
  19. 19.
    Zhao, W.Y., Sawhney, H.S.: Is super-resolution with optical flow feasible? In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part I. LNCS, vol. 2350, pp. 599–613. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Boxin Shi
    • 1
    • 2
  • Hang Zhao
    • 1
  • Moshe Ben-Ezra
    • 1
  • Sai-Kit Yeung
    • 2
  • Christy Fernandez-Cull
    • 3
  • R. Hamilton Shepard
    • 3
  • Christopher Barsi
    • 1
  • Ramesh Raskar
    • 1
  1. 1.MIT Media LabCambridgeUSA
  2. 2.Singapore University of Technology and DesignSingapore
  3. 3.MIT Lincoln LabLexingtonUSA

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