Enhancement of image luminance resolution by imposing random jitter


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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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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  • Silicon retina
  • Super-resolution
  • Statistical neural networks
  • Eye movement