New digital Pulse-Mode Neural Network based image denoising

In this paper, we propose a new architecture of Pulse Mode Neural Network (PMNN) with very simple activation function. Pulse mode is gaining support in the field of hardware Neural Networks thanks to its higher density of integration. However, the complexity of the activation functions presents a drawback for hardware implementation of Neural Networks and limits its area of application. In this context, the main idea is to apply a new kind of activation function, simply generated by the product of two sigmoidal functions, which are very simple and already implemented in previous work. Details of important aspects concerning the hardware implementation are given. To verify the performance and capacity of the proposed design, we apply it for approximation of image denoising function. The filtered results are verified in terms of the Peak Signal to Noise Ratio (PSNR). Experimental results reveal that the proposed PMNN filter has a greater ability to recover the informative pixel intensities from the infected image with a recovery of 7.5 dB for Gaussian noise and 5.3 dB for Speckle noise. Besides, such results demonstrate the performance and efficiency of our Neural filter when compared to other conventional filtering techniques. The designed network is implemented on a field-programmable gate array (FPGA) platform and synthesis results are presented and discussed.