HazeSpikeMamba：结合类脉冲与状态空间特征的自监督真实图像去雾框架
HazeSpikeMamba: Coupling Spiking-Inspired and State-Space Features for Self-Supervised Real-World Dehazing
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中文总结 AI 辅助
本研究提出HazeSpikeMamba框架，结合类脉冲局部路径与注意力状态空间全局路径，采用转导式域适配，在多个真实去雾数据集上取得最优的BRISQUE与NIMA指标
中文摘要 AI 辅助
去雾网络通常在合成的有雾-清晰图像对上训练，但在真实照片上性能常出现下降。采用大气散射模型生成的合成雾无法完全捕捉真实雾的变异性，且配对的真实有雾-清晰图像稀缺。本研究提出HazeSpikeMamba，这是一种紧凑的去雾框架，在多尺度U-Net中结合了类脉冲局部路径与注意力状态空间全局路径。局部路径采用TPCNNSpike，这是一种受脉冲耦合神经网络（PCNN）邻域耦合启发的新型脉冲发射方案；与分组方向扫描不同，TPCNNSpike利用神经元高斯加权邻域的先前激活状态并行更新所有神经元。全局路径适配MambaIRv2的注意力状态空间模块，保留语义提示与序列重排序，同时移除窗口自注意力分支，其状态空间处理以序列长度线性复杂度建模长程依赖。针对目标域适配，在配对NH-HAZE数据上预训练的冻结退化网络，会从去雾预测中重新合成雾；在适配过程中，重建误差仅更新HazeSpikeMamba的最终恢复层，无需无雾标签。共享检查点在每个完整的无标签目标集上仅适配一次，使评估为数据集级转导式适配，而非零样本或逐图像优化。前向网络含202万有效参数，输入为256×256时，经thop测量需13.27G名义MACs。该适配持续提升RTTS、URHI、HSTS上的BRISQUE与NIMA指标：RTTS上BRISQUE从30.13降至27.72，NIMA从4.13升至4.87；在该转导协议下，适配模型在URHI、HSTS的BRISQUE与NIMA指标上也优于所有对比方法。
英文摘要
Dehazing networks are commonly trained on synthetic hazy-clear pairs, but their performance often drops on real photographs. Synthetic haze generated using the atmospheric scattering model does not fully capture the variability of real haze, and paired real hazy-clear images are scarce. In this work, we propose HazeSpikeMamba, a compact dehazing framework that combines a spiking-inspired local path and an attentive state-space global path in a multi-scale U-Net. The local path uses TPCNNSpike, a new spike-emission scheme inspired by the neighborhood coupling of Pulse-Coupled Neural Network (PCNN). Unlike grouped directional scanning, TPCNNSpike updates all neurons in parallel using the previous firing states of their Gaussian-weighted neighborhoods. The global path adapts the Attentive State-Space Module of MambaIRv2, retaining semantic prompting and sequence reordering while removing the window self-attention branch. Its state-space processing models long-range dependencies with complexity linear in sequence length. For target-domain adaptation, a frozen degradation network, pretrained on paired NH-HAZE data, re-synthesizes haze from the dehazed prediction. The reconstruction error updates only the final restoration layers of HazeSpikeMamba without haze-free labels during adaptation. A shared checkpoint is adapted once on each complete unlabeled target set, making the evaluation dataset-level and transductive rather than zero-shot or per-image optimization. The forward network contains 2.02M active parameters and requires 13.27G nominal MACs (measured with thop at 256x256 input). This adaptation consistently improves BRISQUE and NIMA on RTTS, URHI, and HSTS. On RTTS, BRISQUE decreases from 30.13 to 27.72 and NIMA increases from 4.13 to 4.87. Under this transductive protocol, the adapted model also achieves the best BRISQUE and NIMA on URHI and HSTS among the compared methods.
