用脉冲神经网络实现轻量高效去雾，效果媲美顶尖方法
U-Net-Like Spiking Neural Networks for Single Image Dehazing
- 采用类U-Net结构结合脉冲神经网络，兼顾多尺度特征与长程依赖
- 引入正交漏电整合发放模块，提升跨通道通信效率
- 模型更小、计算量更低，适合部署在资源受限设备
图像去雾是计算机视觉中的关键挑战，对提升雾霾环境下图像清晰度至关重要。传统方法依赖大气散射模型，而近期深度学习技术（如卷积神经网络CNN和Transformer）通过有效分析图像特征提升了性能。然而，CNN难以捕捉长距离依赖，Transformer则需大量计算资源。为此，本文提出DehazeSNN，一种融合类U-Net架构与脉冲神经网络（SNNs）的创新模型。该模型能有效捕获多尺度图像特征，同时高效处理局部与长程依赖。引入正交漏电整合发放块（OLIFBlock），增强跨通道通信，显著提升去雾效果并降低计算负担。大量实验表明，DehazeSNN在多个基准数据集上表现优异，生成高质量无雾图像，且模型更小、乘加操作更少。相关代码已开源：https://github.com/HaoranLiu507/DehazeSNN。
原文摘要 · Abstract (English)
Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models, while recent deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Transformers, have improved performance by effectively analyzing image features. However, CNNs struggle with long-range dependencies, and Transformers demand significant computational resources. To address these limitations, we propose DehazeSNN, an innovative architecture that integrates a U-Net-like design with Spiking Neural Networks (SNNs). DehazeSNN captures multi-scale image features while efficiently managing local and long-range dependencies. The introduction of the Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) enhances cross-channel communication, resulting in superior dehazing performance with reduced computational burden. Our extensive experiments show that DehazeSNN is highly competitive to state-of-the-art methods on benchmark datasets, delivering high-quality haze-free images with a smaller model size and less multiply-accumulate operations. The proposed dehazing method is publicly available at https://github.com/HaoranLiu507/DehazeSNN.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。