Multi-channel Memristive Pulse Coupled Neural Network Based Multi-frame Images Super-resolution Reconstruction Algorithm

Zhekang DONG; Chenjie DU; Huipin Lin; Chun sing LAI; Xiaofang HU; Shukai DUAN

doi:10.11999/JEIT190868

Volume 42 Issue 4

Jun. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(4): 835-843

Zhekang DONG, Chenjie DU, Huipin Lin, Chun sing LAI, Xiaofang HU, Shukai DUAN. Multi-channel Memristive Pulse Coupled Neural Network Based Multi-frame Images Super-resolution Reconstruction Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(4): 835-843. doi: 10.11999/JEIT190868

Citation:

Zhekang DONG, Chenjie DU, Huipin Lin, Chun sing LAI, Xiaofang HU, Shukai DUAN. Multi-channel Memristive Pulse Coupled Neural Network Based Multi-frame Images Super-resolution Reconstruction Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(4): 835-843. doi: 10.11999/JEIT190868

Citation:

Zhekang DONG, Chenjie DU, Huipin Lin, Chun sing LAI, Xiaofang HU, Shukai DUAN. Multi-channel Memristive Pulse Coupled Neural Network Based Multi-frame Images Super-resolution Reconstruction Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(4): 835-843. doi: 10.11999/JEIT190868

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Multi-channel Memristive Pulse Coupled Neural Network Based Multi-frame Images Super-resolution Reconstruction Algorithm

doi: 10.11999/JEIT190868

1.
School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China
2.
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
3.
Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom
4.
College of Artificial Intelligence, Southwest University, Chongqing 400715, China

Funds: The National Natural Science Foundation of China (61571394, 61601376), The Fundamental Research Funds for the Provincial Universities (GK199900299012-010)

Received Date: 2019-11-01
Rev Recd Date: 2020-01-12

Available Online: 2020-02-12

Publish Date: 2020-06-04

Abstract

Abstract

The high-resolution image is the prerequisite of information acquisition and precise analysis. Multi-frame super-resolution images reconstruction technologies are able to address many image degraded issues (caused by external shooting environment), such as detail information lost, blurred edges, and so forth. According to the nanoscale memristor, a Multi-channel Memristive Pulse Coupled Neural Network (MMPCNN) model is proposed. This model is able to simulate the adaptive-variable linking coefficient in pulse coupled neural network. Meanwhile, the proposed network is applied to the multi-frame super resolution reconstruction for fusing the registered low resolution images. Furthermore, the sparse coding based super resolution method is performed to improve the original high-resolution image. Finally, a series of computer experiments and the relevant subjective/objective analysis jointly illustrate the validity and effectiveness of the entire scheme.
- Memristor,
- Pulse coupled neural network,
- Multi-frame images,
- Super-resolution reconstruction

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References(20)

References

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