# HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising

arXiv: https://arxiv.org/abs/2404.09697 · HTML: https://mlsanity.com/paper/2404.09697/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Hyperspectral image analysis with Mamba, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 24 of its 42 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Hyperspectral image analysis with Mamba](https://mlsanity.com/ssm/hyperspectral-image-analysis-with-mamba/)

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022)
- [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://mlsanity.com/paper/2402.15648/) (2024)
- [VideoMamba: State Space Model for Efficient Video Understanding](https://mlsanity.com/paper/2403.06977/) (2024)
- [Long Range Language Modeling via Gated State Spaces](https://mlsanity.com/paper/2206.13947/) (2022)
- [Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2111.07910/) (2021)
- [MedMamba: Vision Mamba for Medical Image Classification](https://mlsanity.com/paper/2403.03849/) (2024)
- [Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition](https://mlsanity.com/paper/1707.02477/) (2017)
- [Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network](https://mlsanity.com/paper/1806.00183/) (2018)
- [3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising](https://mlsanity.com/paper/2003.04547/) (2020)
- [Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising](https://mlsanity.com/paper/2304.00844/) (2023)
- [A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration](https://mlsanity.com/paper/2111.09708/) (2021)
- [Spatial-Spectral Transformer for Hyperspectral Image Denoising](https://mlsanity.com/paper/2211.14090/) (2022)
- [Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid](https://mlsanity.com/paper/1908.11754/) (2019)
- [Deep Plug-and-Play Prior for Hyperspectral Image Restoration](https://mlsanity.com/paper/2209.08240/) (2022)
- [Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network](https://mlsanity.com/paper/1810.00495/) (2018)
- [HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification](https://mlsanity.com/paper/2404.00272/) (2024)
- [Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation](https://mlsanity.com/paper/2306.08370/) (2023)
- [Mixed Attention Network for Hyperspectral Image Denoising](https://mlsanity.com/paper/2301.11525/) (2023)

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

- [HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model](https://mlsanity.com/paper/2406.11519/) (2024)
- [Visual Mamba: A Survey and New Outlooks](https://mlsanity.com/paper/2404.18861/) (2024)
- [SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising](https://mlsanity.com/paper/2405.01726/) (2024)
- [Mamba in Vision: A Comprehensive Survey of Techniques and Applications](https://mlsanity.com/paper/2410.03105/) (2024)
- [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025)
- [ConMamba: Contrastive Vision Mamba for Plant Disease Detection](https://mlsanity.com/paper/2506.03213/) (2025)
- [State Space Models Meet Remote Sensing: A Survey](https://mlsanity.com/paper/2606.25329/) (2026)

## Abstract (arXiv)

Effectively modeling global context information in hyperspectral image (HSI) denoising is crucial, but prevailing methods using convolution or transformers still face localized or computational efficiency limitations. Inspired by the emerging Selective State Space Model (Mamba) with nearly linear computational complexity and efficient long-term modeling, we present a novel HSI denoising network named HSIDMamba (HSDM). HSDM is tailored to exploit the capture of potential spatial-spectral dependencies effectively and efficiently for HSI denoising. In particular, HSDM comprises multiple Hyperspectral Continuous Scan Blocks (HCSB) to strengthen spatial-spectral interactions. HCSB links forward and backward scans and enhances information from eight directions through the State Space Model (SSM), strengthening the context representation learning of HSDM and improving denoising performance more effectively. In addition, to enhance the utilization of spectral information and mitigate the degradation problem caused by long-range scanning, spectral attention mechanism. Extensive evaluations against HSI denoising benchmarks validate the superior performance of HSDM, achieving state-of-the-art performance and surpassing the efficiency of the transformer method SERT by 31%.