# O-Mamba: O-shape State-Space Model for Underwater Image Enhancement

arXiv: https://arxiv.org/abs/2408.12816 · HTML: https://mlsanity.com/paper/2408.12816/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Mamba for image restoration, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 25 of its 55 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for image restoration](https://mlsanity.com/ssm/mamba-for-image-restoration/)

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [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)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [Scaling Vision with Sparse Mixture of Experts](https://mlsanity.com/paper/2106.05974/) (2021)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [MACE: Mass Concept Erasure in Diffusion Models](https://mlsanity.com/paper/2403.06135/) (2024)
- [Long Range Language Modeling via Gated State Spaces](https://mlsanity.com/paper/2206.13947/) (2022)
- [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)
- [TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition](https://mlsanity.com/paper/2307.12493/) (2023)
- [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021)
- [Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset](https://mlsanity.com/paper/1811.01343/) (2018)
- [Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration](https://mlsanity.com/paper/2311.16845/) (2023)
- [Underwater Ranker: Learn Which Is Better and How to Be Better](https://mlsanity.com/paper/2208.06857/) (2022)
- [MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts](https://mlsanity.com/paper/2401.04081/) (2024)
- [Shallow-UWnet : Compressed Model for Underwater Image Enhancement](https://mlsanity.com/paper/2101.02073/) (2021)
- [MIGC++: Advanced Multi-Instance Generation Controller for Image Synthesis](https://mlsanity.com/paper/2407.02329/) (2024)
- [Underwater Image Enhancement by Transformer-based Diffusion Model with Non-uniform Sampling for Skip Strategy](https://mlsanity.com/paper/2309.03445/) (2023)
- [Diving Deeper into Underwater Image Enhancement: A Survey](https://mlsanity.com/paper/1907.07863/) (2019)
- [Frequency-Adaptive Pan-Sharpening with Mixture of Experts](https://mlsanity.com/paper/2401.02151/) (2024)
- [Real-Time Multi-Diver Tracking and Re-identification for Underwater Human-Robot Collaboration](https://mlsanity.com/paper/1910.09636/) (2019)

## Built on by (most cited first, 9 of 9)

- [From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective](https://mlsanity.com/paper/2503.13165/) (2025)
- [LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts](https://mlsanity.com/paper/2602.11564/) (2026)
- [From Zero to Detail: A Progressive Spectral Decoupling Paradigm for UHD Image Restoration with New Benchmark](https://mlsanity.com/paper/2604.15654/) (2026)
- [Temporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning](https://mlsanity.com/paper/2607.01667/) (2026)
- [Spiking Pyramid Wavelet Transformation for High-efficient and Low-energy Image Restoration](https://mlsanity.com/paper/2606.18644/) (2026)
- [BSMamba: Brightness and Semantic Modeling for Long-Range Interaction in Low-Light Image Enhancement](https://mlsanity.com/paper/2506.18346/) (2025)
- [VRS-UIE: Value-Driven Reordering Scanning for Underwater Image Enhancement](https://mlsanity.com/paper/2505.01224/) (2025)
- [JDPNet: A Network Based on Joint Degradation Processing for Underwater Image Enhancement](https://mlsanity.com/paper/2512.20213/) (2025)
- [CLUIE: Clustering-Aware Recurrent Propagation with Local Structural Compensation for Underwater Image Enhancement](https://mlsanity.com/paper/2607.21467/) (2026)

## Abstract (arXiv)

Underwater image enhancement (UIE) face significant challenges due to complex underwater lighting conditions. Recently, mamba-based methods have achieved promising results in image enhancement tasks. However, these methods commonly rely on Vmamba, which focuses only on spatial information modeling and struggles to deal with the cross-color channel dependency problem in underwater images caused by the differential attenuation of light wavelengths, limiting the effective use of deep networks. In this paper, we propose a novel UIE framework called O-mamba. O-mamba employs an O-shaped dual-branch network to separately model spatial and cross-channel information, utilizing the efficient global receptive field of state-space models optimized for underwater images. To enhance information interaction between the two branches and effectively utilize multi-scale information, we design a Multi-scale Bi-mutual Promotion Module. This branch includes MS-MoE for fusing multi-scale information within branches, Mutual Promotion module for interaction between spatial and channel information across branches, and Cyclic Multi-scale optimization strategy to maximize the use of multi-scale information. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) results.The code is available at https://github.com/chenydong/O-Mamba.