Improving ‘Objective’ Digital Images with Neuronal Processing: A Computational Approach

This paper describes an experiment where an image recorded with a digital camera is processed using an electro-physiological model of a neuron. The luminosity level of each pixel of the source image is treated as the stimulus for an individual neuron, and the source image is transformed into a respo...

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Bibliographic Details
Published in:International journal of architectural computing 2004-06, Vol.2 (2), p.143-153
Main Authors: Mahalingam, Ganapathy, Kavasseri, Rajesh G.
Format: Article
Language:English
Subjects:
Balancing
Charge coupled devices
Computation
Digital cameras
Luminosity
Mathematical models
Rendering
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Summary:This paper describes an experiment where an image recorded with a digital camera is processed using an electro-physiological model of a neuron. The luminosity level of each pixel of the source image is treated as the stimulus for an individual neuron, and the source image is transformed into a response image based on the processing behavior of the Hodgkin-Huxley neuronal model. It is seen that transformation of the image through neuronal processing yields (i) more evenly balanced levels of luminosity and (ii) a more ‘subjective’ rendering of the environment than what was photographed with the digital camera. The CCD (charge coupled device) – based digital camera reveals its limitation as a linear recording device that does not have a balanced dynamic range. The neuronal processing of the image adds non-linearity and a balanced range to the luminosity levels in the image, rendering it closer to a ‘subjective’ perception of the scene.
ISSN:1478-0771
2048-3988
DOI:10.1260/1478077041518692