Skip to main content
EURASIP Journal on Advances in Signal Processing
EURASIP Journal on Advances in Signal Processing Cover Image
Download PDF

Design of Large Field-of-View High-Resolution Miniaturized Imaging System

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 059546 (2007) Cite this article

Abstract

Steps are taken to design the optical system of lenslet array/photoreceptor array plexus on curved surfaces to achieve a large field of view (FOV) with each lenslet capturing a portion of the scene. An optimal sampling rate in the image plane, as determined by the pixel pitch, is found by the use of an information theoretic performance measure. Since this rate turns out to be sub-Nyquist, superresolution techniques can be applied to the multiple low-resolution (LR) images captured on the photoreceptor array to yield a single high-resolution (HR) image of an object of interest. Thus, the computational imaging system proposed is capable of realizing both the specified resolution and specified FOV.

References

  1. 1.

    Dowski ER Jr., Cathey WT: Extended depth of field through wave-front coding. Applied Optics 1995,34(11):1859-1866. 10.1364/AO.34.001859

    Article  Google Scholar 

  2. 2.

    Adelson EH, Wang JYA: Single lens stereo with a plenoptic camera. IEEE Transactions on Pattern Analysis and Machine Intelligence 1992,14(2):99-106. 10.1109/34.121783

    Article  Google Scholar 

  3. 3.

    Ng R, Levoy M, Brédif M, Duval G, Horowitz M, Hanrahan P: Light field photography with a hand-held plenoptic camera. In Tech. Rep. CTSR 2005-02. Stanford University, Stanford, Calif, USA; 2005. https://doi.org/graphics.stanford.edu/papers/lfcamera/lfcamera-150dpi.pdf

    Google Scholar 

  4. 4.

    Nayar SK: Catadioptric omnidirectional camera. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 1997, San Juan, Puerto Rico, USA 482–488.

    Google Scholar 

  5. 5.

    Kang MG, Chaudhuri S (Eds): Super-resolution image reconstruction In IEEE Signal Processing Magazine 2003,20(3):19-20. 10.1109/MSP.2003.1203206

  6. 6.

    Bose NK, Ahuja NA: Superresolution and noise filtering using moving least squares. IEEE Transactions on Image Processing 2006,15(8):2239-2248.

    Article  Google Scholar 

  7. 7.

    Bose NK, Chan RH, Ng MK (Eds): High-resolution image reconstruction—I In International Journal of Imaging Systems and Technology 2004,14(2):35-89. 10.1002/ima.20005

  8. 8.

    Bose NK, Chan RH, Ng MK (Eds): High-resolution image reconstruction—II In International Journal of Imaging Systems and Technology 2004,14(3):91-145. 10.1002/ima.20012

  9. 9.

    Huck FO, Fales CL, Alter-Gartenberg R, Park SK, Rahman Z: Information-theoretic assessment of sampled imaging systems. Optical Engineering 1999,38(5):742-762. 10.1117/1.602264

    Article  Google Scholar 

  10. 10.

    Kitamura Y, Shogenji R, Yamada K, et al.: Reconstruction of a high-resolution image on a compound-eye image-capturing system. Applied Optics 2004,43(8):1719-1727. 10.1364/AO.43.001719

    Article  Google Scholar 

  11. 11.

    Duparré J, Dannberg P, Schreiber P, Bräuer A, Tünnermann A: Artificial apposition compound eye fabricated by micro-optics technology. Applied Optics 2004,43(22):4303-4310. 10.1364/AO.43.004303

    Article  Google Scholar 

  12. 12.

    Lohmann AW: Scaling laws for lens systems. Applied Optics 1989,28(23):4996-4998. 10.1364/AO.28.004996

    Article  Google Scholar 

  13. 13.

    Born M, Wolf E: Principles of Optics: Electromagnetic Theory of Propogation, Interference and Diffraction of Light. Pergamon Press, Oxford, UK; 1980.

    Google Scholar 

  14. 14.

    Hecht E: Optics. Addison Wesley, New York, NY, USA; 1998.

    Google Scholar 

  15. 15.

    Goodman JW: Introduction to Fourier Optics. 2nd edition. McGraw-Hill, New York, NY, USA; 1996.

    Google Scholar 

  16. 16.

    Fossum ER: Digital camera system on a chip. IEEE Micro 1998,18(3):8-15. 10.1109/40.683047

    Article  Google Scholar 

  17. 17.

    Nakamura J: Basics of image sensors. In Image Sensors and Signal Processing for Digital Still Cameras. Edited by: Nakamura J. Taylor and Francis, London, UK; 2006:53-64.

    Google Scholar 

  18. 18.

    El Gamal A, Eltoukhy H: CMOS image sensors. IEEE Circuits and Devices Magazine 2005,21(3):6-20. 10.1109/MCD.2005.1438751

    Article  Google Scholar 

  19. 19.

    McCluney R: Introduction to Radiometry and Photometry. Artech House, Boston, Mass, USA; 1994.

    Google Scholar 

  20. 20.

    Papoulis A: Probability, Random Variables and Stochastic Processes. 3rd edition. McGraw-Hill, New York, NY, USA; 1991.

    Google Scholar 

Download references

Author information

Affiliations

  1. Department of Electrical Engineering, Spatial and Temporal Signal Processing Center, The Pennsylvania State University, University Park, PA, 16802, USA

    Nilesh A. Ahuja & N. K. Bose

Authors
  1. Nilesh A. Ahuja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. K. Bose
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nilesh A. Ahuja.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Ahuja, N.A., Bose, N.K. Design of Large Field-of-View High-Resolution Miniaturized Imaging System. EURASIP J. Adv. Signal Process. 2007, 059546 (2007). https://doi.org/10.1155/2007/59546

Download citation

Keywords

  • Information Technology
  • Sampling Rate
  • Image System
  • Optical System
  • Image Plane