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A Simple Neural Network for Enhancement of Image Acuity by Fixational Instability

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 5553)

Abstract

Inspired by biological findings, this paper proposes a neural network model for achieving higher image acuity by introducing random eye movement. Statistical analysis and comparison study of the image quality in the presence and absence of random eye movement are carried out using the model. It is revealed that, as a noise source to a stationary image, the random eye movement can contribute to overcome the inherent resolution limits of photoreceptors and enhance sharpness of images by temporal statistics of firing neurons. Super-resolution and prominent edges can thus be achieved, with superior visual acuity to the absence of eye-movement. The acuity enhancement is in fact a trade-off between bias and variance and is related to the distribution of visual stimuli and eye-movement patterns. The simulations illustrate its effect on enhancement of image acuity.

Keywords

  • Super-resolution
  • Image acuity
  • Eye-movement
  • Statistical neural networks

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  • DOI: 10.1007/978-3-642-01513-7_31
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© 2009 Springer-Verlag Berlin Heidelberg

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Yi, D., Jiang, P., Zhu, J. (2009). A Simple Neural Network for Enhancement of Image Acuity by Fixational Instability. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_31

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  • DOI: https://doi.org/10.1007/978-3-642-01513-7_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01512-0

  • Online ISBN: 978-3-642-01513-7

  • eBook Packages: Computer ScienceComputer Science (R0)