# MAMAT: 3D Mamba-Based Atmospheric Turbulence Removal and its Object Detection Capability

arXiv: https://arxiv.org/abs/2503.17700 · HTML: https://mlsanity.com/paper/2503.17700/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 27 of its 36 references that are in the map.
- First posted on arXiv 22 Mar 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Builds on (most cited first, 27 of 27)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020)
- [TOOD: Task-aligned One-stage Object Detection](https://mlsanity.com/paper/2108.07755/) (2021)
- [You Only Learn One Representation: Unified Network for Multiple Tasks](https://mlsanity.com/paper/2105.04206/) (2021)
- [Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform](https://mlsanity.com/paper/2107.11627/) (2021)
- [nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model](https://mlsanity.com/paper/2402.03526/) (2024)
- [Imaging through the Atmosphere using Turbulence Mitigation Transformer](https://mlsanity.com/paper/2207.06465/) (2022)
- [Learning to restore images degraded by atmospheric turbulence using uncertainty](https://mlsanity.com/paper/2207.03447/) (2022)
- [Subsampled Turbulence Removal Network](https://mlsanity.com/paper/1807.04418/) (2018)
- [Spatio-Temporal Turbulence Mitigation: A Translational Perspective](https://mlsanity.com/paper/2401.04244/) (2024)
- [Atmospheric Turbulence Removal with Complex-Valued Convolutional Neural Network](https://mlsanity.com/paper/2204.06989/) (2022)
- [ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulence](https://mlsanity.com/paper/1910.03119/) (2019)
- [Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion](https://mlsanity.com/paper/1808.03550/) (2018)
- [Atmospheric turbulence removal using convolutional neural network](https://mlsanity.com/paper/1912.11350/) (2019)
- [Deep Learning Techniques for Atmospheric Turbulence Removal: A Review](https://mlsanity.com/paper/2409.14587/) (2024)
- [Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence](https://mlsanity.com/paper/2404.13605/) (2024)
- [DeTurb: Atmospheric Turbulence Mitigation with Deformable 3D Convolutions and 3D Swin Transformers](https://mlsanity.com/paper/2407.20855/) (2024)
- [Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models](https://mlsanity.com/paper/2204.08745/) (2022)
- [Object recognition in atmospheric turbulence scenes](https://mlsanity.com/paper/2210.14318/) (2022)
- [Real-World Atmospheric Turbulence Correction via Domain Adaptation](https://mlsanity.com/paper/2402.07371/) (2024)
- [Detection of moving objects through turbulent media. Decomposition of Oscillatory vs Non-Oscillatory spatio-temporal vector fields](https://mlsanity.com/paper/2410.21551/) (2024)

## Built on by (most cited first, 5 of 5)

- [DMAT: An End-to-End Framework for Joint Atmospheric Turbulence Mitigation and Object Detection](https://mlsanity.com/paper/2507.04323/) (2025)
- [JDATT: A Joint Distillation Framework for Atmospheric Turbulence Mitigation and Target Detection](https://mlsanity.com/paper/2507.19780/) (2025)
- [RMFAT: Recurrent Multi-scale Feature Atmospheric Turbulence Mitigator](https://mlsanity.com/paper/2508.11409/) (2025)
- [GSTurb: Gaussian Splatting for Atmospheric Turbulence Mitigation](https://mlsanity.com/paper/2602.22800/) (2026)
- [ReMATF: Recurrent Motion-Adaptive Multi-scale Turbulence Mitigation for Dynamic Scenes](https://mlsanity.com/paper/2605.21440/) (2026)

## Abstract (arXiv)

Restoration and enhancement are essential for improving the quality of videos captured under atmospheric turbulence conditions, aiding visualization, object detection, classification, and tracking in surveillance systems. In this paper, we introduce a novel Mamba-based method, the 3D Mamba-Based Atmospheric Turbulence Removal (MAMAT), which employs a dual-module strategy to mitigate these distortions. The first module utilizes deformable 3D convolutions for non-rigid registration to minimize spatial shifts, while the second module enhances contrast and detail. Leveraging the advanced capabilities of the 3D Mamba architecture, experimental results demonstrate that MAMAT outperforms state-of-the-art learning-based methods, achieving up to a 3% improvement in visual quality and a 15% boost in object detection. It not only enhances visualization but also significantly improves object detection accuracy, bridging the gap between visual restoration and the effectiveness of surveillance applications.