# Underwater Image Enhancement by Convolutional Spiking Neural Networks

arXiv: https://arxiv.org/abs/2503.20485 · HTML: https://mlsanity.com/paper/2503.20485/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 87 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Going Deeper With Directly-Trained Larger Spiking Neural Networks](https://mlsanity.com/paper/2011.05280/) (2020)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/1903.06530/) (2019)
- [RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2003.01811/) (2020)
- [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021)
- [Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures](https://mlsanity.com/paper/1903.06379/) (2019)
- [WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images](https://mlsanity.com/paper/1702.07392/) (2017)
- [U-shape Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2111.11843/) (2021)
- [Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratch](https://mlsanity.com/paper/2010.01729/) (2020)
- [Underwater Ranker: Learn Which Is Better and How to Be Better](https://mlsanity.com/paper/2208.06857/) (2022)
- [Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation](https://mlsanity.com/paper/2110.07742/) (2021)
- [Uncertainty Inspired Underwater Image Enhancement](https://mlsanity.com/paper/2207.09689/) (2022)
- [PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators](https://mlsanity.com/paper/2306.08918/) (2023)
- [Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer](https://mlsanity.com/paper/1710.07084/) (2017)
- [An Analytical Estimation of Spiking Neural Networks Energy Efficiency](https://mlsanity.com/paper/2210.13107/) (2022)
- [Shallow-UWnet : Compressed Model for Underwater Image Enhancement](https://mlsanity.com/paper/2101.02073/) (2021)
- [A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/2104.10719/) (2021)
- [Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration](https://mlsanity.com/paper/2106.07910/) (2021)
- [Spiking Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.17046/) (2023)
- [Learning A Spiking Neural Network for Efficient Image Deraining](https://mlsanity.com/paper/2405.06277/) (2024)
- [Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons](https://mlsanity.com/paper/1911.08261/) (2019)
- [Keys to Accurate Feature Extraction Using Residual Spiking Neural Networks](https://mlsanity.com/paper/2111.05955/) (2021)
- [Spiking neural networks for nonlinear regression](https://mlsanity.com/paper/2210.03515/) (2022)
- [A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation](https://mlsanity.com/paper/2407.04230/) (2024)

## Built on by (most cited first, 3 of 3)

- [snnTrans-DHZ: A Lightweight Spiking Neural Network Architecture for Underwater Image Dehazing](https://mlsanity.com/paper/2504.11482/) (2025)
- [Aquatic Neuromorphic Optical Flow](https://mlsanity.com/paper/2605.07653/) (2026)
- [UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement](https://mlsanity.com/paper/2605.08376/) (2026)

## Abstract (arXiv)

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×) improvement in energy efficiency. The source code is available at hrefhttps://github.com/vidya-rejul/UIE-SNN.gitUIE-SNN.