# IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model

arXiv: https://arxiv.org/abs/2405.09873 · HTML: https://mlsanity.com/paper/2405.09873/

## Key facts

- Cited by 13 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 29 of its 60 references that are in the map.
- First posted on arXiv 16 May 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, 29 of 29)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Activating More Pixels in Image Super-Resolution Transformer](https://mlsanity.com/paper/2205.04437/) (2022)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://mlsanity.com/paper/2402.15648/) (2024)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Exploring Models and Data for Remote Sensing Image Caption Generation](https://mlsanity.com/paper/1712.07835/) (2017)
- [Feedback Network for Image Super-Resolution](https://mlsanity.com/paper/1903.09814/) (2019)
- [MambaOut: Do We Really Need Mamba for Vision?](https://mlsanity.com/paper/2405.07992/) (2024)
- [Low-Light Image Enhancement with Wavelet-based Diffusion Models](https://mlsanity.com/paper/2306.00306/) (2023)
- [Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary](https://mlsanity.com/paper/2401.08209/) (2024)
- [MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection](https://mlsanity.com/paper/2403.02148/) (2024)
- [State Space Model for New-Generation Network Alternative to Transformers: A Survey](https://mlsanity.com/paper/2404.09516/) (2024)
- [Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution](https://mlsanity.com/paper/2302.13800/) (2023)
- [A Survey on Visual Mamba](https://mlsanity.com/paper/2404.15956/) (2024)
- [ShuffleMixer: An Efficient ConvNet for Image Super-Resolution](https://mlsanity.com/paper/2205.15175/) (2022)
- [Recursive Generalization Transformer for Image Super-Resolution](https://mlsanity.com/paper/2303.06373/) (2023)
- [Low-light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion](https://mlsanity.com/paper/2401.03788/) (2024)
- [Infrared Image Super-Resolution: Systematic Review, and Future Trends](https://mlsanity.com/paper/2212.12322/) (2022)
- [LKFormer: Large Kernel Transformer for Infrared Image Super-Resolution](https://mlsanity.com/paper/2401.11859/) (2024)

## Built on by (most cited first, 13 of 13)

- [CWNet: Causal Wavelet Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2507.10689/) (2025)
- [Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey](https://mlsanity.com/paper/2505.23248/) (2025)
- [MODEM: A Morton-Order Degradation Estimation Mechanism for Adverse Weather Image Recovery](https://mlsanity.com/paper/2505.17581/) (2025)
- [FTMoMamba: Motion Generation with Frequency and Text State Space Models](https://mlsanity.com/paper/2411.17532/) (2024)
- [GPSMamba: A Global Phase and Spectral Prompt-guided Mamba for Infrared Image Super-Resolution](https://mlsanity.com/paper/2507.18998/) (2025)
- [MoEMba: A Mamba-based Mixture of Experts for High-Density EMG-based Hand Gesture Recognition](https://mlsanity.com/paper/2502.17457/) (2025)
- [BSMamba: Brightness and Semantic Modeling for Long-Range Interaction in Low-Light Image Enhancement](https://mlsanity.com/paper/2506.18346/) (2025)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [GTFMN: Guided Texture and Feature Modulation Network for Low-Light Image Enhancement and Super-Resolution](https://mlsanity.com/paper/2601.19157/) (2026)
- [FSCM: Frequency-Enhanced Spatial-Spectral Coupled Mamba for Infrared Hyperspectral Image Colorization](https://mlsanity.com/paper/2605.15880/) (2026)
- [State Space Models Meet Remote Sensing: A Survey](https://mlsanity.com/paper/2606.25329/) (2026)
- [IR275K: A Benchmark for Infrared Multi-Frame Super-Resolution Toward Efficient Remote Sensing](https://mlsanity.com/paper/2607.22380/) (2026)
- [TIRMamba: A Thermal-Prior-Modulated State-Space Network for Sub-Million-Parameter Infrared Image Super-Resolution](https://mlsanity.com/paper/2610.05182/) (2026)

## Abstract (arXiv)

Infrared image super-resolution demands long-range dependency modeling and multi-scale feature extraction to address challenges such as homogeneous backgrounds, weak edges, and sparse textures. While Mamba-based state-space models (SSMs) excel in global dependency modeling with linear complexity, their block-wise processing disrupts spatial consistency, limiting their effectiveness for IR image reconstruction. We propose IRSRMamba, a novel framework integrating wavelet transform feature modulation for multi-scale adaptation and an SSMs-based semantic consistency loss to restore fragmented contextual information. This design enhances global-local feature fusion, structural coherence, and fine-detail preservation while mitigating block-induced artifacts. Experiments on benchmark datasets demonstrate that IRSRMamba outperforms state-of-the-art methods in PSNR, SSIM, and perceptual quality. This work establishes Mamba-based architectures as a promising direction for high-fidelity IR image enhancement. Code are available at https://github.com/yongsongH/IRSRMamba.