A Signal-Processing Neural Model Based on Biological Retina
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 ce...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/1/35 |
| _version_ | 1811187364183146496 |
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| author | Hui Wei Luping Wang Shanshan Wang Yuxiang Jiang Jingmeng Li |
| author_facet | Hui Wei Luping Wang Shanshan Wang Yuxiang Jiang Jingmeng Li |
| author_sort | Hui Wei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T14:02:06Z |
| format | Article |
| id | doaj.art-fb96d4e30d18407cbc56377947f63fc5 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T14:02:06Z |
| publishDate | 2019-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-fb96d4e30d18407cbc56377947f63fc52022-12-22T04:20:07ZengMDPI AGElectronics2079-92922019-12-01913510.3390/electronics9010035electronics9010035A Signal-Processing Neural Model Based on Biological RetinaHui Wei0Luping Wang1Shanshan Wang2Yuxiang Jiang3Jingmeng Li4Laboratory of Cognitive Model and Algorithm, Department of Computer Science, Fudan University, No. 825 Zhang Heng Road, Shanghai 201203, ChinaLaboratory of Cognitive Model and Algorithm, Department of Computer Science, Fudan University, No. 825 Zhang Heng Road, Shanghai 201203, ChinaIntel Asia-Pacific Research Development Ltd., No. 880 Zi Xing Road, Shanghai 200241, ChinaLaboratory of Cognitive Model and Algorithm, Department of Computer Science, Fudan University, No. 825 Zhang Heng Road, Shanghai 201203, ChinaLaboratory of Cognitive Model and Algorithm, Department of Computer Science, Fudan University, No. 825 Zhang Heng Road, Shanghai 201203, ChinaImage 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.https://www.mdpi.com/2079-9292/9/1/35signal representationrivalry pathwayvisual processing |
| spellingShingle | Hui Wei Luping Wang Shanshan Wang Yuxiang Jiang Jingmeng Li A Signal-Processing Neural Model Based on Biological Retina Electronics signal representation rivalry pathway visual processing |
| title | A Signal-Processing Neural Model Based on Biological Retina |
| title_full | A Signal-Processing Neural Model Based on Biological Retina |
| title_fullStr | A Signal-Processing Neural Model Based on Biological Retina |
| title_full_unstemmed | A Signal-Processing Neural Model Based on Biological Retina |
| title_short | A Signal-Processing Neural Model Based on Biological Retina |
| title_sort | signal processing neural model based on biological retina |
| topic | signal representation rivalry pathway visual processing |
| url | https://www.mdpi.com/2079-9292/9/1/35 |
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