Name
Abstract | ||
|---|---|---|
Pulse-coupled neural network (PCNN) aims to control neuronal firing state automatically and complete related image processing tasks. This paper presents a pulse-number-adjustable MSPCNN model (PNA-MSPCNN) that can automatically acquire the firing times and the firing frequency of each neuron. Hereinto, synaptic weight matrix W-ijkl and decay factor alpha will generate an interaction value to determine the final calculation result of the internal activity U. Dynamic threshold amplitude V, step function Q, and auxiliary parameter P can precisely adjust the variation ranges of the dynamic threshold E. Additionally, we propose a low-light image enhancement method based on the above PNA-MSPCNN and a modified low-light image enhancement (LIME). The proposed LIME algorithm focuses mainly on the parameter setting method of weight matrix W-mq, which will bring further improvement of testing image contrast. Experimental results demonstrate that our proposed method achieves better low-light image enhancement performances, compared to prevalent image enhancement methods, including SSIM of 0.8725, AMBE of 0.0550, MSE of 0.0092, and PSNR of 45.7764. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ACCESS.2021.3132078 | IEEE ACCESS |
Keywords | DocType | Volume |
Pulse-coupled neural network, low-light image enhancement method, automatic parameter, setting method, neuronal firing times, neuronal firing frequency | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Lian | 1 | 0 | 0.34 |
| Jizhao Liu | 2 | 12 | 2.26 |
| Zhen Yang | 3 | 0 | 3.04 |
| Yunliang Qi | 4 | 1 | 2.03 |
| Huaikun Zhang | 5 | 0 | 0.34 |
| Mingxuan Zhang | 6 | 0 | 0.34 |
| Yide Ma | 7 | 34 | 12.10 |