Advertisement

Neural Computing and Applications

, Volume 20, Issue 2, pp 261–272 | Cite as

Enhancement of image luminance resolution by imposing random jitter

  • Daqing Yi
  • Ping Jiang
  • Edward Mallen
  • Xiaonian Wang
  • Jin Zhu
Original Article

Abstract

Inspired by biological eyes, silicon retinas with pixel-level processing have been developed to achieve very high-speed and high-quality image processing. Due to the limitation on the fill factor and the dimension of a silicon chip, both spatial and luminance resolutions have to be kept low. For recovering fine images from a silicon retina with a lower resolution, the authors propose a neural network model and its electronic counterpart by imposing random jitter to the sensor and collecting temporal statistics of the firing neurons. Statistical analysis shows that the scheme can enhance resolution of an image and emphasize contrast edges present in the image. It is further proved that the enhancement in luminance resolution and sharpness is a trade-off between recovering bias and variance. Therefore, jitter intensity needs to be optimized by considering the luminance distribution. The simulations illustrate its effect on the fine detail reconstruction using the proposed scheme.

Keywords

Silicon retina Super-resolution Statistical neural networks Eye movement 

References

  1. 1.
    Lee LP, Szema R (2005) Inspirations from biological optics for advanced photonic systems. Science 310(5751):1148–1150CrossRefGoogle Scholar
  2. 2.
    Feng J (2003) Computational neuroscience: a comprehensive approach. CRC Press, Boca RatonCrossRefGoogle Scholar
  3. 3.
    Cronin TW, Marshall J (2001) Parallel processing and image analysis in the eyes of mantis shrimps. Biol Bul 200:177–183CrossRefGoogle Scholar
  4. 4.
    Ohta J (2007) Smart CMOS image sensors. CRC Press, Boca RatonCrossRefGoogle Scholar
  5. 5.
    Mead C, Mahowald MA (1988) A silicon model of early visual processing. Neural Netw 1:91–97CrossRefGoogle Scholar
  6. 6.
    Culurciello E, Etienne-Cummings R, Boahen KA (2003) A biomorphic digital image sensor. IEEE J Solid-State Circuits 38(2):281–294CrossRefGoogle Scholar
  7. 7.
    Boahen K (2005) Neuromorphic microchips. Sci Am 292:56–63CrossRefGoogle Scholar
  8. 8.
    Kurino H, Nakagawa M, Lee KW, Nakamura T, Yamada Y, Park KT, Koyanagi M (2001) Vision chip fabricated by using three dimensional integration technology. IEIC Tech Rep (Institute of Electronics, Information and Communication Engineers) 101(85):29–35Google Scholar
  9. 9.
    Forchheimer R, Åström A (1994) Near-sensor image processing: a new paradigm. IEEE Trans Image Process 3(6):736–746CrossRefGoogle Scholar
  10. 10.
    Bernard TM, Nguyen PE, Devos FJ, Zavidovique BY (1993) A programmable VLSI retina for rough vision. Mach Vis Appl 7(1):4–11CrossRefGoogle Scholar
  11. 11.
    Chen K, Åström A, Danielsson PE (1990) PASIC: a smart sensor for computer vision. Proceedings of the 10th international conference on pattern recognition, pp 286–291Google Scholar
  12. 12.
    Fowler B, El Gamal A, Yang DXD (1994) A CMOS area image sensor with pixel-level A/D conversion. Proceedings of the IEEE international solid state circuits conference, pp 226–227Google Scholar
  13. 13.
    Ditchburn RW, Ginsborg BL (1953) Involuntary eye movements during fixation. J Physiol 119(1):1–17Google Scholar
  14. 14.
    Ginsborg BL, Maurice DM (1959) Involuntary movements of the eye during fixation and blinking. Br J Ophthalmol 43(7):435–437CrossRefGoogle Scholar
  15. 15.
    Martinez-Conde S, Macknik SL, Hubel DH (2004) The role of fixational eye movements in visual perception. Nat Rev Neurosci 5:229–240CrossRefGoogle Scholar
  16. 16.
    Martinez-Conde S, Macknik SL, Hubel DH (2000) Microsaccadic eye movements and firing of single cells in the striate cortex of macaque monkeys. Nat Neurosci 3:251–258CrossRefGoogle Scholar
  17. 17.
    Pitkow X, Sompolinsky H, Meister M (2007) A neural computation for visual acuity in the presence of eye movements. PLoS Biol 5(12):e331CrossRefGoogle Scholar
  18. 18.
    Miller JA, Denning KS, George JS, Marshak DW, Kenyon GT (2006) A high frequency resonance in the responses of retinal ganglion cells to rapidly modulated stimuli: a computer model. Vis Neurosci 23(5):779–794CrossRefGoogle Scholar
  19. 19.
    Greschner M, Bingard M, Rujan P, Ammermuller J (2002) Retinal ganglion cell synchronization by fixational eye movements improves feature estimation. Nat Neurosci 5:341–347CrossRefGoogle Scholar
  20. 20.
    Rucci M, Iovin R, Poletti M, Santini F (2007) Miniature eye movements enhance fine spatial detail. Nature 447(7146):851–854CrossRefGoogle Scholar
  21. 21.
    Donner K, Hemil S (2007) Modelling the effect of microsaccades on retinal responses to stationary contrast patterns. Vis Res 47(9):1166–1177CrossRefGoogle Scholar
  22. 22.
    Propokopowicz P, Cooper P (1995) The dynamic retina. Int J Comput Vis 16:191–204CrossRefGoogle Scholar
  23. 23.
    Landolt O, Mitros A (2001) Visual sensor with resolution enhancement by mechanical vibrations. Auton Robots 11(3):233–239zbMATHCrossRefGoogle Scholar
  24. 24.
    Hongler M, de Meneses YL, Beyeler A, Jacot J (2003) The resonant retina: exploiting vibration noise to optimally detect edges in an image. IEEE Trans Pattern Anal Mach Intell 25(9):1051–1062CrossRefGoogle Scholar
  25. 25.
    Demler MJ (1991) High-speed analog-to-digital conversion. Academic Press, Inc., New YorkGoogle Scholar
  26. 26.
    Horn BKP (1998) Robot vision. MIT Press, CambridgeGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Daqing Yi
    • 1
  • Ping Jiang
    • 2
  • Edward Mallen
    • 3
  • Xiaonian Wang
    • 1
  • Jin Zhu
    • 1
  1. 1.School of Electronics and Information EngineeringTongji UniversityShanghaiChina
  2. 2.School of InformaticsUniversity of BradfordBradfordUK
  3. 3.School of Optometry and Vision ScienceUniversity of BradfordBradfordUK

Personalised recommendations