Neuromorphic decoding of sample image representations by the boundary-consistent interpolation method

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The paper discusses methods for encoding and decoding large amounts of data using a neuromorphic model based on known neuromechanisms for the perception of visual information. Known mechanisms of the visual system, such as aggregation of counts by receptive fields, central-lateral inhibition, etc., have been studied. A decoding model has been developed that implements the function of simple cells of the primary visual cortex responsible for spatial perception of stimulus contrasts. The proposed decoding model makes it possible to restore local boundaries of objects in an image, while improving the visual quality of images in comparison with the quality of restoration with classical bilinear interpolation.

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Sobre autores

V. Kershner

Kotel’nikov Institute of Radio Engineering and Electronics, Russian Academy of Sciences

Autor responsável pela correspondência
Email: vladkershner@mail.ru
Rússia, Mokhovaya Str., 11, Build. 7, Moscow, 125009

Bibliografia

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2. Fig. 1. Representation of an image based on a sample of samples (sample representation): a – original image “butterfly-19” [8], b – sample representation of 10 million samples.

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3. Fig. 2. Partitioning of the image surface Ω by a system of receptive ON-fields with square carriers ∆k ∪ ∆s located at the nodes of a regular square lattice.

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4. Fig. 3. Illustration of the coding procedure (11) on a 50×50 receptive field grid of a selective representation of the image “butterfly-19” [8] (see Fig. 1): a – selective representation, b – RP with non-zero values ​​of δ, ON responses (δ > 0) are highlighted in white, OFF responses (δ < 0) are highlighted in black.

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5. Fig. 4. The reconstructed (decoded) image “butterfly-19” [8] (see Fig. 1), defined by the selective representation {} on the 50×50 lattice: a – smoothed image, decoded only using the “smooth” part {nk} of the code, b – interpolation along the edges defined by the details {δk}.

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