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Neuromorphic Image Coding/Decoding Based on Sampling Estimates of the Local Contrast.

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Title: Neuromorphic Image Coding/Decoding Based on Sampling Estimates of the Local Contrast.
Authors: Antsiperov, V. E., Kershner, V. A.
Source: Pattern Recognition & Image Analysis; Dec2024, Vol. 34 Issue 4, p1129-1140, 12p
Abstract: Neuromorphic methods for processing relatively large volumes of streaming data using the example of an image encoding/restoration problem are discussed. For these purposes, a special (sampling) representation of the input data is used in the form of a stream of discrete events (counts), similar to the events of discharges of neurons in the retina of the eye. Based on the specificity of sampling representation, a generative data model is naturally formalized in the form of a system of components distributed across the field of view—a system of receptive fields that embody the universal principles (including lateral inhibition) of biological neural networks. The mechanism of lateral inhibition is realized in the model in the form of an antagonistic structure of receptive fields center/surround. Issues of image decoding are considered in the context of restoration of spatial contrasts, which partly mimics the work of the so-called simple cells of the visual cortex. It is shown that the coupled ON–OFF decoding model allows for the restoration of sharp image details in the form of emphasizing contrast boundaries. The procedures of neuromorphic encoding/decoding of video data synthesized in this work can be used in modern communication systems, as well as in related tasks of searching, identifying, etc. objects in digital images. [ABSTRACT FROM AUTHOR]
Copyright of Pattern Recognition & Image Analysis is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Neuromorphic Image Coding/Decoding Based on Sampling Estimates of the Local Contrast.
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  Data: Pattern Recognition & Image Analysis; Dec2024, Vol. 34 Issue 4, p1129-1140, 12p
– Name: Abstract
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  Group: Ab
  Data: Neuromorphic methods for processing relatively large volumes of streaming data using the example of an image encoding/restoration problem are discussed. For these purposes, a special (sampling) representation of the input data is used in the form of a stream of discrete events (counts), similar to the events of discharges of neurons in the retina of the eye. Based on the specificity of sampling representation, a generative data model is naturally formalized in the form of a system of components distributed across the field of view—a system of receptive fields that embody the universal principles (including lateral inhibition) of biological neural networks. The mechanism of lateral inhibition is realized in the model in the form of an antagonistic structure of receptive fields center/surround. Issues of image decoding are considered in the context of restoration of spatial contrasts, which partly mimics the work of the so-called simple cells of the visual cortex. It is shown that the coupled ON–OFF decoding model allows for the restoration of sharp image details in the form of emphasizing contrast boundaries. The procedures of neuromorphic encoding/decoding of video data synthesized in this work can be used in modern communication systems, as well as in related tasks of searching, identifying, etc. objects in digital images. [ABSTRACT FROM AUTHOR]
– Name: Abstract
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  Data: <i>Copyright of Pattern Recognition & Image Analysis is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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