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Two-stage Mamba-based diffusion model for image restoration.

Lei Liu^1,2, Luan Ma¹, Shuai Wang^3,4

¹School of Computer Science and Technology, Huaibei Normal University, Huaibei, 235000, China.

Scientific Reports

|July 2, 2025

Summary

This study introduces Diff-Mamba, a novel image restoration model. Diff-Mamba effectively restores degraded images using a Mamba-based diffusion approach, outperforming existing transformer and diffusion methods.

Keywords:

Diffusion Mamba Image deblurring Image denoising Image deraining Image restoration

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Image restoration is crucial in computer vision for enhancing degraded images.
Transformer and diffusion models show promise but have limitations: transformers are computationally expensive, and diffusion models can suffer from inaccurate noise estimation.
Existing methods struggle with balancing efficiency and performance in image restoration tasks.

Purpose of the Study:

To propose Diff-Mamba, a novel two-stage adaptive Mamba-based diffusion model for superior image restoration.
To integrate the efficient State Space Model (Mamba) into diffusion models for image restoration and generation.
To enhance image restoration by improving both inference and training efficiency and accuracy.

Main Methods:

Developed Diff-Mamba, a two-stage adaptive model combining a diffusion state space model (DSSM) and a diffusion feedforward neural network (DFNN).
DSSM leverages Mamba's linear complexity and diffusion models' representational power for efficient and effective image restoration.
DFNN optimizes information flow through depthwise convolutional layers to capture finer image details and local structures.

Main Results:

Diff-Mamba demonstrated superior performance in image deraining, denoising, and deblurring tasks compared to state-of-the-art diffusion and transformer-based methods.
The model achieved competitive restoration quality across various standard image datasets.
Extensive experiments validated the effectiveness and efficiency of the proposed Diff-Mamba architecture.

Conclusions:

Diff-Mamba offers a significant advancement in image restoration by effectively addressing the limitations of previous approaches.
The integration of Mamba into diffusion models provides a powerful and efficient framework for visual data generation and restoration.
The proposed method sets a new benchmark for image restoration tasks, offering a promising direction for future research.