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Open AccessArticle

A Signal-Processing Neural Model Based on Biological Retina

Laboratory of Cognitive Model and Algorithm, Department of Computer Science, Fudan University, No. 825 Zhang Heng Road, Shanghai 201203, China
School of Mechanical Engineering, University of Shanghai for Science and Technology, No. 516 Jun Gong Road, Shanghai 200093, China
Intel Asia-Pacific Research Development Ltd., No. 880 Zi Xing Road, Shanghai 200241, China
Author to whom correspondence should be addressed.
Electronics 2020, 9(1), 35;
Received: 23 October 2019 / Revised: 14 December 2019 / Accepted: 24 December 2019 / Published: 27 December 2019
(This article belongs to the Special Issue Bioinspired Computer Vision)
Image signal processing has considerable value in artificial intelligence. However, due to the diverse disturbance (e.g., color, noise), the image signal processing, especially the representation of the signal, remains a big challenge. In the human visual system, it has been justified that simple cells in the primary visual cortex are obviously sensitive to vision signals with partial orientation features. In other words, the image signals are extracted and described along the pathway of visual processing. Inspired by this neural mechanism of the primary visual cortex, it is possible to build an image signal-processing model as the neural architecture. In this paper, we presented a method to process the image signal involving a multitude of disturbance. For image signals, we first extracted 4 rivalry pathways via the projection of color. Secondly, we designed an algorithm in which the computing process of the stimulus with partial orientation features can be altered into a process of analytical geometry, resulting in that the signals with orientation features can be extracted and characterized. Finally, through the integration of characterizations from the 4 different rivalry pathways, the image signals can be effectively interpreted and reconstructed. Instead of data-driven methods, the presented approach requires no prior training. With the use of geometric inferences, the method tends to be interpreted and applied in the signal processor. The extraction and integration of rivalry pathways of different colors allow the method to be effective and robust to the signals with the image noise and disturbance of colors. Experimental results showed that the approach can extract and describing the image signal with diverse disturbance. Based on the characterization of the image signal, it is possible to reconstruct signal features which can effectively represent the important information from the original image signal. View Full-Text
Keywords: signal representation; rivalry pathway; visual processing signal representation; rivalry pathway; visual processing
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Wei, H.; Wang, L.; Wang, S.; Jiang, Y.; Li, J. A Signal-Processing Neural Model Based on Biological Retina. Electronics 2020, 9, 35.

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