# WaterMamba: Visual State Space Model for Underwater Image Enhancement

arXiv: https://arxiv.org/abs/2405.08419 · HTML: https://mlsanity.com/paper/2405.08419/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://mlsanity.com/paper/2402.15648/) (2024)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021)
- [U-shape Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2111.11843/) (2021)
- [Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset](https://mlsanity.com/paper/1811.01343/) (2018)
- [Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions](https://mlsanity.com/paper/1901.05320/) (2019)
- [VmambaIR: Visual State Space Model for Image Restoration](https://mlsanity.com/paper/2403.11423/) (2024)
- [Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank](https://mlsanity.com/paper/2303.09101/) (2023)
- [Uncertainty Inspired Underwater Image Enhancement](https://mlsanity.com/paper/2207.09689/) (2022)
- [Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception](https://mlsanity.com/paper/2002.01155/) (2020)
- [PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators](https://mlsanity.com/paper/2306.08918/) (2023)

## Built on by (most cited first, 7 of 7)

- [Mamba in Vision: A Comprehensive Survey of Techniques and Applications](https://mlsanity.com/paper/2410.03105/) (2024)
- [PixMamba: Leveraging State Space Models in a Dual-Level Architecture for Underwater Image Enhancement](https://mlsanity.com/paper/2406.08444/) (2024)
- [MODEM: A Morton-Order Degradation Estimation Mechanism for Adverse Weather Image Recovery](https://mlsanity.com/paper/2505.17581/) (2025)
- [Mamba-UIE: Enhancing Underwater Images with Physical Model Constraint](https://mlsanity.com/paper/2407.19248/) (2024)
- [JDPNet: A Network Based on Joint Degradation Processing for Underwater Image Enhancement](https://mlsanity.com/paper/2512.20213/) (2025)
- [DY-LUT: Depth-Aware YCbCr Lookup Tables for Real-Time Underwater Image Enhancement](https://mlsanity.com/paper/2607.22801/) (2026)
- [State-Isolated Vision Mamba via Update-Tendency Routing for Underwater Image Enhancement](https://mlsanity.com/paper/2610.13720/) (2026)

## Abstract (arXiv)

Underwater imaging often suffers from low quality due to factors affecting light propagation and absorption in water. To improve image quality, some underwater image enhancement (UIE) methods based on convolutional neural networks (CNN) and Transformer have been proposed. However, CNN-based UIE methods are limited in modeling long-range dependencies, and Transformer-based methods involve a large number of parameters and complex self-attention mechanisms, posing efficiency challenges. Considering computational complexity and severe underwater image degradation, a state space model (SSM) with linear computational complexity for UIE, named WaterMamba, is proposed. We propose spatial-channel omnidirectional selective scan (SCOSS) blocks comprising spatial-channel coordinate omnidirectional selective scan (SCCOSS) modules and a multi-scale feedforward network (MSFFN). The SCOSS block models pixel and channel information flow, addressing dependencies. The MSFFN facilitates information flow adjustment and promotes synchronized operations within SCCOSS modules. Extensive experiments showcase WaterMamba's cutting-edge performance with reduced parameters and computational resources, outperforming state-of-the-art methods on various datasets, validating its effectiveness and generalizability. The code will be released on GitHub after acceptance.