基于U-Net架构的脉冲神经网络单幅图像去雾方法

摘要

图像去雾是计算机视觉领域的关键挑战，对提升雾霾条件下图像清晰度至关重要。传统方法多依赖于大气散射模型，而近期深度学习技术——特别是卷积神经网络（CNN）和Transformer——通过有效分析图像特征提升了去雾性能。然而CNN难以捕捉长程依赖关系，Transformer则需大量计算资源。为突破这些局限，我们提出DehazeSNN这一创新架构，将类U-Net设计与脉冲神经网络（SNN）相融合。该模型能捕获多尺度图像特征，同时高效处理局部与长程依赖关系。我们引入的正交漏积分发放电模块（OLIFBlock）增强了跨通道通信能力，在降低计算负担的同时实现了卓越的去雾性能。大量实验表明，DehazeSNN在基准数据集上与国际领先方法相比具有显著竞争力，能以更小的模型规模和更少的乘加运算量生成高质量无雾图像。本去雾方法已开源於：https://github.com/HaoranLiu507/DehazeSNN。

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.

基于U-Net架构的脉冲神经网络单幅图像去雾方法

U-Net-Like Spiking Neural Networks for Single Image Dehazing

摘要

Support