Neural information coding for efficient spike-based image denoising
Andrea Castagnetti Alain Pegatoquet Benoît Miramond
In recent years, Deep Convolutional Neural Networks (DCNNs) have outreached
the performance of classical algorithms for image restoration tasks. However
most of these methods are not suited for computational efficiency and are
therefore too expensive to be executed on embedded and mobile devices. In this
work we investigate Spiking Neural Networks (SNNs) for Gaussian denoising, with
the goal of approaching the performance of conventional DCNN while reducing the
computational load. We propose a formal analysis of the information conversion
processing carried out by the Leaky Integrate and Fire (LIF) neurons and we
compare its performance with the classical rate-coding mechanism. The neural
coding schemes are then evaluated through experiments in terms of denoising
performance and computation efficiency for a state-of-the-art deep
convolutional neural network. Our results show that SNNs with LIF neurons can
provide competitive denoising performance but at a reduced computational cost.