SNNSIR: A Simple Spiking Neural Network for Stereo Image Restoration

• URL: http://arxiv.org/abs/2508.12271v1
• Date: Sun, 17 Aug 2025 07:38:25 GMT
• Title: SNNSIR: A Simple Spiking Neural Network for Stereo Image Restoration
• Abstract summary: Spiking Neural Networks (SNNs) offer high computational efficiency and low energy consumption.<n>We propose SNNSIR, a simple yet effective Spiking Neural Network for Stereo Image Restoration.<n>Our model achieves competitive restoration performance while significantly reducing computational overhead.
• Score: 46.28078168687418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking Neural Networks (SNNs), characterized by discrete binary activations, offer high computational efficiency and low energy consumption, making them well-suited for computation-intensive tasks such as stereo image restoration. In this work, we propose SNNSIR, a simple yet effective Spiking Neural Network for Stereo Image Restoration, specifically designed under the spike-driven paradigm where neurons transmit information through sparse, event-based binary spikes. In contrast to existing hybrid SNN-ANN models that still rely on operations such as floating-point matrix division or exponentiation, which are incompatible with the binary and event-driven nature of SNNs, our proposed SNNSIR adopts a fully spike-driven architecture to achieve low-power and hardware-friendly computation. To address the expressiveness limitations of binary spiking neurons, we first introduce a lightweight Spike Residual Basic Block (SRBB) to enhance information flow via spike-compatible residual learning. Building on this, the Spike Stereo Convolutional Modulation (SSCM) module introduces simplified nonlinearity through element-wise multiplication and highlights noise-sensitive regions via cross-view-aware modulation. Complementing this, the Spike Stereo Cross-Attention (SSCA) module further improves stereo correspondence by enabling efficient bidirectional feature interaction across views within a spike-compatible framework. Extensive experiments on diverse stereo image restoration tasks, including rain streak removal, raindrop removal, low-light enhancement, and super-resolution demonstrate that our model achieves competitive restoration performance while significantly reducing computational overhead. These results highlight the potential for real-time, low-power stereo vision applications. The code will be available after the article is accepted.

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