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Enhancement of image luminance resolution by imposing random jitter

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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.

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References

  1. Lee LP, Szema R (2005) Inspirations from biological optics for advanced photonic systems. Science 310(5751):1148–1150

    Article  Google Scholar 

  2. Feng J (2003) Computational neuroscience: a comprehensive approach. CRC Press, Boca Raton

    Book  Google Scholar 

  3. Cronin TW, Marshall J (2001) Parallel processing and image analysis in the eyes of mantis shrimps. Biol Bul 200:177–183

    Article  Google Scholar 

  4. Ohta J (2007) Smart CMOS image sensors. CRC Press, Boca Raton

    Book  Google Scholar 

  5. Mead C, Mahowald MA (1988) A silicon model of early visual processing. Neural Netw 1:91–97

    Article  Google Scholar 

  6. Culurciello E, Etienne-Cummings R, Boahen KA (2003) A biomorphic digital image sensor. IEEE J Solid-State Circuits 38(2):281–294

    Article  Google Scholar 

  7. Boahen K (2005) Neuromorphic microchips. Sci Am 292:56–63

    Article  Google Scholar 

  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):29–35

    Google Scholar 

  9. Forchheimer R, Åström A (1994) Near-sensor image processing: a new paradigm. IEEE Trans Image Process 3(6):736–746

    Article  Google Scholar 

  10. Bernard TM, Nguyen PE, Devos FJ, Zavidovique BY (1993) A programmable VLSI retina for rough vision. Mach Vis Appl 7(1):4–11

    Article  Google Scholar 

  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 286–291

  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 226–227

  13. Ditchburn RW, Ginsborg BL (1953) Involuntary eye movements during fixation. J Physiol 119(1):1–17

    Google Scholar 

  14. Ginsborg BL, Maurice DM (1959) Involuntary movements of the eye during fixation and blinking. Br J Ophthalmol 43(7):435–437

    Article  Google Scholar 

  15. Martinez-Conde S, Macknik SL, Hubel DH (2004) The role of fixational eye movements in visual perception. Nat Rev Neurosci 5:229–240

    Article  Google Scholar 

  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 3:251–258

    Article  Google Scholar 

  17. Pitkow X, Sompolinsky H, Meister M (2007) A neural computation for visual acuity in the presence of eye movements. PLoS Biol 5(12):e331

    Article  Google Scholar 

  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):779–794

    Article  Google Scholar 

  19. Greschner M, Bingard M, Rujan P, Ammermuller J (2002) Retinal ganglion cell synchronization by fixational eye movements improves feature estimation. Nat Neurosci 5:341–347

    Article  Google Scholar 

  20. Rucci M, Iovin R, Poletti M, Santini F (2007) Miniature eye movements enhance fine spatial detail. Nature 447(7146):851–854

    Article  Google Scholar 

  21. Donner K, Hemil S (2007) Modelling the effect of microsaccades on retinal responses to stationary contrast patterns. Vis Res 47(9):1166–1177

    Article  Google Scholar 

  22. Propokopowicz P, Cooper P (1995) The dynamic retina. Int J Comput Vis 16:191–204

    Article  Google Scholar 

  23. Landolt O, Mitros A (2001) Visual sensor with resolution enhancement by mechanical vibrations. Auton Robots 11(3):233–239

    Article  MATH  Google Scholar 

  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):1051–1062

    Article  Google Scholar 

  25. Demler MJ (1991) High-speed analog-to-digital conversion. Academic Press, Inc., New York

    Google Scholar 

  26. Horn BKP (1998) Robot vision. MIT Press, Cambridge

    Google Scholar 

Download references

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Shanghai, China

    Daqing Yi, Xiaonian Wang & Jin Zhu

  2. School of Informatics, University of Bradford, Bradford, UK

    Ping Jiang

  3. School of Optometry and Vision Science, University of Bradford, Bradford, UK

    Edward Mallen

Authors
  1. Daqing Yi
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  2. Ping Jiang
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  3. Edward Mallen
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  4. Xiaonian Wang
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  5. Jin Zhu
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Corresponding author

Correspondence to Ping Jiang.

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Yi, D., Jiang, P., Mallen, E. et al. Enhancement of image luminance resolution by imposing random jitter. Neural Comput & Applic 20, 261–272 (2011). https://doi.org/10.1007/s00521-010-0433-1

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  • DOI: https://doi.org/10.1007/s00521-010-0433-1

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