Abstract
Underwater image enhancement (UIE) is fundamental for marine applications, including autonomous vision-based navigation. Deep learning methods using convolutional neural networks (CNN) and vision transformers advanced UIE performance. Recently, spiking neural networks (SNN) have gained attention for their lightweight design, energy efficiency, and scalability. This paper introduces UIE-SNN, the first SNN-based UIE algorithm to improve visibility of underwater images. UIE-SNN is a 19- layered convolutional spiking encoder-decoder framework with skip connections, directly trained using surrogate gradient-based backpropagation through time (BPTT) strategy. We explore and validate the influence of training datasets on energy reduction, a unique advantage of UIE-SNN architecture, in contrast to the conventional learning-based architectures, where energy consumption is model-dependent. UIE-SNN optimizes the loss function in latent space representation to reconstruct clear underwater images. Our algorithm performs on par with its non-spiking counterpart methods in terms of PSNR and structural similarity index (SSIM) at reduced timesteps ($T=5$) and energy consumption of $85\%$. The algorithm is trained on two publicly available benchmark datasets, UIEB and EUVP, and tested on unseen images from UIEB, EUVP, LSUI, U45, and our custom UIE dataset. The UIE-SNN algorithm achieves PSNR of \(17.7801~dB\) and SSIM of \(0.7454\) on UIEB, and PSNR of \(23.1725~dB\) and SSIM of \(0.7890\) on EUVP. UIE-SNN achieves this algorithmic performance with fewer operators (\(147.49\) GSOPs) and energy (\(0.1327~J\)) compared to its non-spiking counterpart (GFLOPs = \(218.88\) and Energy=\(1.0068~J\)). Compared with existing SOTA UIE methods, UIE-SNN achieves an average of \(6.5\times\) improvement in energy efficiency. The source code is available at \href{this https URL}{UIE-SNN}.
Abstract (translated)
水下图像增强（UIE）对于海洋应用至关重要，包括自主视觉导航。使用卷积神经网络（CNN）和视觉变换器的深度学习方法提升了UIE性能。近期，脉冲神经网络（SNN）因其轻量级设计、能源效率及可扩展性而备受关注。本文介绍了UIE-SNN，一种基于SNN的首个水下图像增强算法，旨在提高水下图像的可见度。UIE-SNN是一个包含19层卷积脉冲编码器-解码器框架，并通过跳连结构直接使用基于替代梯度的时间反向传播（BPTT）策略进行训练。我们探讨并验证了训练数据集对能耗减少的影响，这突显了UIE-SNN架构的独特优势，在传统学习架构中，能源消耗依赖于模型本身。UIE-SNN在潜在空间表示中优化损失函数以重构清晰的水下图像。 我们的算法在PSNR（峰值信噪比）和结构相似性指数（SSIM）方面与非脉冲版本的方法相当，但在减少时间步长（$T=5$）和能耗（85%）的情况下表现良好。该算法使用两个公开基准数据集UIEB和EUVP进行训练，并在来自UIEB、EUVP、LSUI、U45以及我们自定义的UIE数据集中未见过的图像上进行了测试。UIE-SNN在UIEB上的PSNR为$17.7801\,dB$，SSIM为$0.7454$；而在EUVP上的PSNR为$23.1725\,dB$，SSIM为$0.7890$。与非脉冲版本相比，UIE-SNN使用更少的操作（GSOPs = $147.49$）和能量消耗（$0.1327~J$），而非脉冲版本的GFLOPs = $218.88$且能耗为$1.0068~J$。相较于现有的UIE先进方法，UIE-SNN实现了平均$6.5\times$的能效提升。 该算法的源代码可在[UIE-SNN](this https URL)获取。
URL
https://arxiv.org/abs/2503.20485
