A Pulse-Number-Adjustable MSPCNN and Its Image Enhancement Application

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 $W_{\mathrm {ijkl}}$ 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 $W_{\mathrm {mq}}$ , 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.

Keywords

• Pulse-coupled neural network
• low-light image enhancement method
• automatic parameter setting method
• neuronal firing times
• neuronal firing frequency