Recent Advances in Pulse-Coupled Neural Networks with Applications in Image Processing
Abstract
:1. Introduction
2. Fundamentals of PCNN
2.1. Original Pulse-Coupled Neural Network
2.2. Intersecting Cortical Model
2.3. Spiking Cortical Model
2.4. Modified SPCNN
2.5. Fire-Controlled MSPCNN
2.6. Sine–Cosine PCNN
2.7. Quasi-Continuous Model
2.8. Heterogeneous PCNN
- Neurons with different weights, but the same structure.
- Neurons with different structures, but the same weight.
- Both structure and weight are different for different neurons.
2.9. Continuous-Coupled Neural Network
3. Applications
3.1. Color Image Processing
3.2. Diagnosis and Computer Vision
3.3. Image Fusion
3.4. Other Recent Advances
3.5. Summary
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Liu, H.; Liu, M.; Li, D.; Zheng, W.; Yin, L.; Wang, R. Recent Advances in Pulse-Coupled Neural Networks with Applications in Image Processing. Electronics 2022, 11, 3264. https://doi.org/10.3390/electronics11203264
Liu H, Liu M, Li D, Zheng W, Yin L, Wang R. Recent Advances in Pulse-Coupled Neural Networks with Applications in Image Processing. Electronics. 2022; 11(20):3264. https://doi.org/10.3390/electronics11203264
Chicago/Turabian StyleLiu, Haoran, Mingzhe Liu, Dongfen Li, Wenfeng Zheng, Lirong Yin, and Ruili Wang. 2022. "Recent Advances in Pulse-Coupled Neural Networks with Applications in Image Processing" Electronics 11, no. 20: 3264. https://doi.org/10.3390/electronics11203264
APA StyleLiu, H., Liu, M., Li, D., Zheng, W., Yin, L., & Wang, R. (2022). Recent Advances in Pulse-Coupled Neural Networks with Applications in Image Processing. Electronics, 11(20), 3264. https://doi.org/10.3390/electronics11203264