Interleaving and Sparse Random Coded Aperture for Lens-Free Visible Imaging

  • Zhenglin Wang
  • Ivan Lee
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 298)

Abstract

Coded aperture has been applied to short wavelength imaging (e.g., gamma-ray), and it suffers from diffraction and interference for taking longer wavelength images. This paper investigates an interleaving and sparse random (ISR) coded aperture to reduce the impact of diffraction and interference for visible imaging. The interleaving technique treats coded aperture as a combination of many small replicas to reduce the diffraction effects and to increase the angular resolution. The sparse random coded aperture reduces the interference effects by increasing the separations between adjacent open elements. These techniques facilitate the analysis of the imaging model based only on geometric optics. Compressed sensing is applied to recover the coded image by coded aperture, and a physical prototype is developed to examine the proposed techniques.

Keywords

Computational imaging Coded aperture imaging Image reconstruction techniques 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dicke, R.H.: Scatter-Hole Cameras for X-Rays and Gamma Rays. The Astrophysical Journal 153, L101–L106 (1968)CrossRefGoogle Scholar
  2. 2.
    Gottesman, S.R.: Coded apertures: past, present, and future application and design. In: Proc. SPIE, vol. 6714, pp. 1–11 (2007)Google Scholar
  3. 3.
    Fenimore, E.E., Cannon, T.M.: Coded Aperture Imaging with Uniformly Redundant Arrays. Appl. Opt. 17, 337–347 (1978)CrossRefGoogle Scholar
  4. 4.
    Slinger, C., Eismann, M., Gordon, N., Lewis, K., McDonald, G., McNie, M., Payne, D., Ridley, K., Strens, M., De Villiers, G., Wilson, R.: An investigation of the potential for the use of a high resolution adaptive coded aperture system in the mid-wave infrared. In: Proc. SPIE, vol. 6714, pp. 1–12 (2007)Google Scholar
  5. 5.
    Ridley, D., Villiers, D., Payne, A., Wilson, A., Slinger, W.: Visible band lens-free imaging using coded aperture technique. In: Proc. SPIE, vol. 7468, pp. 1–10 (2009)Google Scholar
  6. 6.
    Gottesman, S.R., Isser, A., Gigioli, J.G.W.: Adaptive coded aperture imaging: progress and potential future applications. In: Proc. SPIE, vol. 8165, pp. 1–9 (2011)Google Scholar
  7. 7.
    Byard, K.: Index class apertures-a class of flexible coded aperture. Appl. Opt. 51, 3453–3460 (2012)CrossRefGoogle Scholar
  8. 8.
    Gottesman, S.R., Schneid, E.J.: PNP - A New Class of Coded Aperture Arrays. IEEE Trans. Nucl. Sci. 33, 745–749 (1986)CrossRefGoogle Scholar
  9. 9.
    Gourlay, A.R., Stephen, J.B.: Geometric coded aperture masks. Appl. Opt. 22, 4042–4047 (1983)CrossRefGoogle Scholar
  10. 10.
    Gottesman, S.R., Fenimore, E.: New family of binary arrays for coded aperture imaging. Appl. Opt. 28, 4344–4352 (1989)CrossRefGoogle Scholar
  11. 11.
    Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52, 1289–1306 (2006)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Candès, E.J.: Compressive sampling. In: Proceedings of the International Congress of Mathematicians, Madrid, Spain, pp. 1433–1452 (2006)Google Scholar
  13. 13.
    Wagadarikar, A., John, R., Willett, R., Brady, D.: Single disperser design for coded aperture snapshot spectral imaging. Appl. Opt. 47, 44–51 (2008)CrossRefGoogle Scholar
  14. 14.
    Marcia, R.F., Harmany, Z.T., Willett, R.M.: Compressive coded aperture imaging. In: Proc. SPIE, vol. 7246, pp. 1–13 (2009)Google Scholar
  15. 15.
    Llull, P., Liao, X., Yuan, X., Yang, J., Kittle, D., Carin, L., Sapiro, G., Brady, D.: Coded aperture compressive temporal imaging. Opt. Express 21, 526–545 (2013)CrossRefGoogle Scholar
  16. 16.
    Caroli, E., Stephen, J.B., Dicocco, G., Natalucci, L., Spizzichino, A.: Coded Aperture Imaging in X-Ray and Gamma-Ray Astronomy. Space Sci. Rev. 45, 349–403 (1987)CrossRefGoogle Scholar
  17. 17.
    Young, M.: Pinhole Optics. Appl. Opt. 10, 2763–2767 (1971)CrossRefGoogle Scholar
  18. 18.
    Berinde, R., Indyk, P.: Sparse recovery using sparse random matrices. MIT-CSAIL Technical Report (2008)Google Scholar
  19. 19.
    Born, M., Wolf, E.: Principles of optics: electromagnetic theory of propagation, interference and diffraction of light. Cambridge U. Press (1999)Google Scholar
  20. 20.
    Duarte, M.F., Davenport, M.A., Takhar, D., Laska, J.N., Sun, T., Kelly, K.F., Baraniuk, R.G.: Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag. 25, 83–91 (2008)CrossRefGoogle Scholar
  21. 21.
    Candes, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics 59, 1207–1223 (2006)CrossRefMATHMathSciNetGoogle Scholar
  22. 22.
    Bajwa, W.U., Haupt, J.D., Raz, G.M., Wright, S.J., Nowak, R.D.: Toeplitz-Structured Compressed Sensing Matrices. In: Proceedings of IEEE/SP 14th Workshop on Statistical Signal Processing, pp. 294–298 (2007)Google Scholar
  23. 23.
    Figueiredo, M., Nowak, R., Wright, S.: Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems. IEEE J. Sel. Topics Signal Process. 1, 586–597 (2007)CrossRefGoogle Scholar
  24. 24.
    Patsakis, C., Aroukatos, N.: LSB and DCT steganographic detection using compressive sensing. Journal of Information Hiding and Multimedia Signal Processing 5(1), 20–32 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zhenglin Wang
    • 1
  • Ivan Lee
    • 1
  1. 1.School of Information Technology and Mathematical SciencesUniversity of South AustraliaMawson LakesAustralia

Personalised recommendations