发表机构
Zhongnan University of Economics and Law; Mohamed bin Zayed University of Artificial Intelligence; Yunnan University; National Institute of Natural Hazards, Ministry of Emergency Management of China; Wuhan University（中南财经政法大学; 穆罕默德·本·扎耶德人工智能大学; 云南大学; 中国应急管理部国家自然灾害防治研究院; 武汉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对脉冲神经网络在遥感图像去雾中受雾霾高频衰减与脉冲阈值耦合限制的问题，提出EM-SNN框架，集成TM-LIF神经元与SSM模块，在保持事件驱动稀疏性的同时提升去雾性能，能耗仅为强ANN基线SFRDP-Net的四分之一。
AI 中文摘要
尽管脉冲神经网络（SNN）为人工神经网络（ANN）提供了一种节能的替代方案，但其在遥感图像去雾中的应用仍然有限。一个关键挑战源于雾霾引起的高频衰减与离散脉冲阈值之间的耦合。这种相互作用抑制了弱响应，从根本上限制了脉冲去雾模型中边缘、纹理和细节的恢复。为了解决这一挑战，我们提出了高效调制脉冲神经网络（EM-SNN），这是一个专为遥感图像去雾定制的脉冲框架。EM-SNN集成了基于统计的阈值调制泄漏积分激发（TM-LIF）神经元，以自适应补偿雾霾引起的对比度压缩，以及尖峰索贝尔调制（SSM）模块，该模块增强结构线索并减少脉冲特征传播过程中的深度衰减。通过联合调制激活尺度和结构表示，EM-SNN提高了去雾性能，同时保持了SNN固有的事件驱动稀疏性。在HRSD、RICE、RRSHID和SateHaze1K上的实验表明，EM-SNN在实现具有竞争力的去雾性能的同时，仅消耗强ANN基线SFRDP-Net四分之一的能量。
英文摘要
Although spiking neural networks (SNNs) provide an energy-efficient alternative to artificial neural networks (ANNs), their application to remote sensing image dehazing remains limited. A key challenge arises from the coupling between haze-induced high-frequency attenuation and discrete spike thresholding. This interaction suppresses weak responses and fundamentally limits the recovery of edges, textures, and fine details in spiking dehazing models. To address this challenge, we propose the Efficiently Modulated Spiking Neural Network (EM-SNN), a dedicated spiking framework tailored to remote sensing image dehazing. EM-SNN integrates a statistics-driven Threshold-Modulated Leaky Integrate-and-Fire (TM-LIF) neuron to adaptively compensate for haze-induced contrast compression, together with a Spike Sobel Modulation (SSM) module that enhances structural cues and reduces depth-wise attenuation during spiking feature propagation. By jointly modulating activation scales and structural representations, EM-SNN improves dehazing performance while preserving the inherent event-driven sparsity of SNNs. Experiments on HRSD, RICE, RRSHID, and SateHaze1K demonstrate that EM-SNN achieves competitive dehazing performance while consuming only one quarter of the energy of the strong ANN baseline SFRDP-Net.
