awesome-mamba-in-low-level-vision
github.com/csguoh/awesome-mamba-in-low-level-vision ↗A paper list of recent mamba efforts for low-level vision.
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Image RestorationAll-in-one Image RestorationImage Super-resolutionImage DerainingImage DehazingImage DeblurringLow-light Image EnhancementImage CompressionImage FusionRemote Sensing ImageHyperspectral ImageUnderWater ImageImage Quality AssessmentMedical ImageVideo RestorationOthersContributing to this repo
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me image super-resolution resources from awesome-mamba-in-low-level-vision"
Installation instructions →What's inside
Image Super-resolution
- A Collaborative Network of Mamba and CNN for Lightweight Image Super-Resolution
- DVMSR: Distillated Vision Mamba for Efficient Super-Resolution
- First-order State Space Model for Lightweight Image Super-resolution
- HMSR: Hypercomplex-Guided Mamba for Fine-Texture Coupling in Single Image Super-Resolution
- IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model
- Keyframe-Centric State-Space Modeling for Burst Image Super-Resolution
Image Deraining
All-in-one Image Restoration
Image Fusion
- A novel state space model with local enhancement and state sharing for image fusion
- FMamba: Multimodal Image Fusion Driven by State Space Models
- Fusionmamba: Dynamic feature enhancement for multimodal image fusion with mamba
- FusionMamba: Efficient Remote Sensing Image Fusion with State Space Model
- Interactive Spatial-Frequency Fusion Mamba for Multi-Modal Image Fusion
- Mambadfuse: A mamba-based dual-phase model for multi-modality image fusion
Hyperspectral Image
- Bidirectional-Aware Network Combining Transformer and Mamba for Hyperspectral Image Denoising
- Dual Hyperspectral Mamba for Efficient Spectral Compressive Imaging
- Hsidmamba: Exploring bidirectional state-space models for hyperspectral denoising
- HSRMamba: Contextual Spatial-Spectral State Space Model for Single Hyperspectral Super-Resolution
Image Compression
Image Restoration
- Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision
- CU-Mamba: Selective State Space Models with Channel Learning for Image Restoration
- Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration
- EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
- Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution
- MaIR: A Locality and Continuity Preserving Mamba for Image Restoration
Contributing to this repo
Showing a sample of 74 resources. View the full list on GitHub →