SpikeRain: An Efficient Single Image Deraining Technique Based on Spiking Neural Networks

SpikeRain is an open-source tool that uses Spiking Neural Networks (SNN) to achieve single image deraining, published at the WACV 2026 conference. Compared to traditional CNN methods, SNN has event-driven and low-power characteristics, providing a new technical path for the image restoration field. It addresses the quality issues of rainy images and is suitable for edge AI scenarios.

Rainy images often suffer from quality degradation, affecting visual effects and subsequent computer vision tasks like target detection and autonomous driving perception. Traditional deraining methods based on CNN are effective but have high computational overhead. SNN, as the third-generation neural network, differs from CNN in information transmission—CNN uses continuous values while SNN uses discrete spikes. It is event-driven, low-power, and biologically inspired by the human brain's spike communication mechanism.

Single image deraining faces multiple challenges: 1. Ill-posed problem (infinite possible solutions for restoring rain-free images); 2. Rain diversity (including drops, streaks, fog, and motion blur); 3. Background protection (avoiding texture loss or edge blur from over-deraining); 4. Real-time requirement (critical for practical applications like autonomous driving).

SpikeRain's design targets the above challenges: 1. Event-driven deraining mechanism (selectively responds to significant changes like rain edges, reducing computation via sparse activation); 2. Temporal information utilization (iterative refinement over multiple time steps to protect background details); 3. Energy efficiency optimization (threshold learning, time coding optimization, and hardware-aware design for neuromorphic chips like Intel Loihi).

SpikeRain provides a user-friendly Windows application. System requirements: Windows 10+ (64-bit recommended), 4GB+ memory, 2GHz+ processor, 500MB disk space. Usage steps: download and install from GitHub Releases, load the rainy image, start processing, save the result. Advanced settings include adjusting deraining intensity, output format, and preview mode.

Advantages over CNN methods: higher energy efficiency (ideal for edge devices), natural temporal consistency (potential for video deraining), biological rationality, and synergy with neuromorphic hardware. Limitations: training difficulty (non-differentiable spikes require surrogate gradient methods), slight performance gap vs CNN, immature toolchain (smaller community and fewer resources), and dependency on specialized neuromorphic chips.

Application scenarios: autonomous driving (low power and real-time), outdoor solar-powered monitoring (long continuous operation), mobile devices (local fast processing), and drone aerial photography (resource-constrained edge processing). Academic value: validates SNN's applicability in low-level vision tasks, promotes neuromorphic vision research, and integrates neuroscience, computer vision, and hardware engineering.

SpikeRain is open-source with code available for researchers to reproduce results, precompiled binaries for non-technical users, and continuous updates via GitHub. Conclusion: SpikeRain balances efficiency and practicality, showing SNN's potential in edge AI. Future prospects include more innovative applications as neuromorphic hardware matures and SNN training algorithms improve.