Underwater Image Enhancement by Convolutional Spiking Neural Networks

2025-03-26

AI Summary

This work addresses underwater image enhancement (UIE) by proposing UIE-SNN, the first end-to-end spiking neural network (SNN) framework for this task—marking the inaugural application of SNNs to UIE. The architecture employs a 19-layer convolutional encoder-decoder with skip connections and a latent-space reconstruction loss, trained via surrogate-gradient-based backpropagation through time (BPTT). Key contributions include: (i) uncovering the mechanistic relationship between training data distribution and SNN energy consumption; (ii) achieving state-of-the-art performance on UIEB and EUVP benchmarks—PSNR of 17.78/23.17 dB and SSIM of 0.745/0.789; and (iii) enabling ultra-efficient inference with only five time steps, consuming merely 0.1327 J (an 85% reduction versus CNNs), yielding a 6.5× improvement in energy efficiency and requiring only 147.49 GSOPs of computational throughput.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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 imes) improvement in energy efficiency. The source code is available at href{https://github.com/vidya-rejul/UIE-SNN.git}{UIE-SNN}.

Problem

• Enhancing underwater images using spiking neural networks
• Reducing energy consumption in underwater image processing
• Improving visibility and quality of marine application images

Innovation

• Convolutional Spiking Neural Networks for UIE
• Surrogate gradient-based BPTT training strategy
• Energy-efficient latent space optimization