  Recently, spiking neural networks (SNNs) have demonstrated substantial
potential in computer vision tasks. In this paper, we present an Efficient
Spiking Deraining Network, called ESDNet. Our work is motivated by the
observation that rain pixel values will lead to a more pronounced intensity of
spike signals in SNNs. However, directly applying deep SNNs to image deraining
task still remains a significant challenge. This is attributed to the
information loss and training difficulties that arise from discrete binary
activation and complex spatio-temporal dynamics. To this end, we develop a
spiking residual block to convert the input into spike signals, then adaptively
optimize the membrane potential by introducing attention weights to adjust
spike responses in a data-driven manner, alleviating information loss caused by
discrete binary activation. By this way, our ESDNet can effectively detect and
analyze the characteristics of rain streaks by learning their fluctuations.
This also enables better guidance for the deraining process and facilitates
high-quality image reconstruction. Instead of relying on the ANN-SNN conversion
strategy, we introduce a gradient proxy strategy to directly train the model
for overcoming the challenge of training. Experimental results show that our
approach gains comparable performance against ANN-based methods while reducing
energy consumption by 54%. The code source is available at
https://github.com/MingTian99/ESDNet.
