csguoh/Awesome-Mamba-in-Low-Level-Vision

A paper list of recent mamba efforts for low-level vision.

Awesome-Mamba-in-Low-Level-Vision

News

• 2024-07-02 MambaIR is accepted by ECCV 2024!
• 2024-07-30 FreqMamba, VmambaSCI, Wave-Mamba, RainMamba, ALGNet have been accepted by ACM MM 2024. Congratulations!

Table of Contents

• Image Restoration
• Image Super-resolution
• Image Deraining
• Image Dehazing
• Image Deblurring
• Low-light Image Enhancement
• Image Compression
• Image Fusion
• Remote Sensing Image
• Hyperspectral Image
• UnderWater Image
• Image Quality Assessment
• Medical Image
• Video Restoration

Image Restoration

• MambaIR: A Simple Baseline for Image Restoration with State-space Model (ECCV 2024)
  

• Vmambair: Visual State Space Model for Image Restoration (Mar, 2024)
  

• Serpent: Scalable and Efficient Image Restoration via Multi-scale Structured State Space Models (Mar, 2024)
  

• CU-Mamba: Selective State Space Models with Channel Learning for Image Restoration (Apr, 2024)
  

Image Super-resolution

• DVMSR: Distillated Vision Mamba for Efficient Super-Resolution (May, 2024)
  

• IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model (May, 2024)
  

Image Deraining

• FreqMamba: Viewing Mamba from a Frequency Perspective for Image Deraining (ACM MM 2024)
  

• Image Deraining with Frequency-Enhanced State Space Model (May, 2024)
  

• FourierMamba: Fourier Learning Integration with State Space Models for Image Deraining (June, 2024)
  

Image Dehazing

• U-shaped Vision Mamba for Single Image Dehazing (Feb, 2024)
  

Image Deblurring

• Efficient Visual State Space Model for Image Deblurring (May, 2024)
  

• Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring (ACM MM 2024)
  

Low-light Image Enhancement

• Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement (May, 2024)
  

• MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space (May, 2024)
  

• RESVMUNetX: A Low-Light Enhancement Network Based on VMamba (July, 2024)
  

• Wave-Mamba: Wavelet State Space Model for Ultra-High-Definition Low-Light Image Enhancement (ACM MM 2024)
  

Image Compression

• MambaVC: Learned Visual Compression with Selective State Spaces (May, 2024)
  

Image Fusion

• Mambadfuse: A mamba-based dual-phase model for multi-modality image fusion (Apr, 2024)
  

• A novel state space model with local enhancement and state sharing for image fusion (Apr, 2024)
  

• Fusionmamba: Dynamic feature enhancement for multimodal image fusion with mamba (May, 2024)
  

Remote Sensing Image

• RSDehamba: Lightweight Vision Mamba for Remote Sensing Satellite Image Dehazing (May, 2024)
  

• Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution (May, 2024)
  

Hyperspectral Image

• SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising (May, 2024)
  

• Hsidmamba: Exploring bidirectional state-space models for hyperspectral denoising (Apr, 2024)
  

• Dual Hyperspectral Mamba for Efficient Spectral Compressive Imaging (June, 2024)
  

UnderWater Image

• WaterMamba: Visual State Space Model for Underwater Image Enhancement (May, 2024)
  

• PixMamba: Leveraging State Space Models in a Dual-Level Architecture for Underwater Image Enhancement (June, 2024)
  

Image Quality Assessment

• Q-Mamba: On First Exploration of Vision Mamba for Image Quality Assessment (June, 2024)
  

Medical Image

• Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution (July, 2024)
  

• Deform-Mamba Network for MRI Super-Resolution (July, 2024)
  

Video Restoration

• RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining (ACM MM 2024)
  

Others

• LFMamba: Light Field Image Super-Resolution with State Space Model (June, 2024)
  

• VmambaSCI: Dynamic Deep Unfolding Network with Mamba for Compressive Spectral Imaging (ACM MM 2024)
  

Contributing to this repo

• Here is the tutorial of contributing to others projects.
• First, think about which category the work should belong to.
• Second, use the same format as the others to describe the work. Note that there should be two spaces between the title and the arixv icon. You can follow the previous readme format to creat new arxiv/github icons by simply replacing the url.

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