EM-SNN: Efficiently Modulated Spiking Neural Network for Remote Sensing Image Dehazing

Key points

• Although spiking neural networks (SNNs) provide an energy-efficient alternative to artificial neural networks (ANNs), their application to remote sensing image dehazing remains limited.1
• A key challenge arises from the coupling between haze-induced high-frequency attenuation and discrete spike thresholding.1
• 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.1
• By jointly modulating activation scales and structural representations, EM-SNN improves dehazing performance while preserving the inherent event-driven sparsity of SNNs.1

Sources (1)

• [1]EM-SNN: Efficiently Modulated Spiking Neural Network for Remote Sensing Image Dehazing
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 01:14 AM

  To address this challenge, we propose the Efficiently Modulated Spiking Neural Network (EM-SNN), a dedicated spiking framework tailored to remote sensing image dehazing.

  Although spiking neural networks (SNNs) provide an energy-efficient alternative to artificial neural networks (ANNs), their application to remote sensing image dehazing remains limited.

Extractive summary: sentences quoted from the sources.