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Adverse-Weather Image Restoration Method Based on VMT-Net.

Zhongmin Liu¹, Xuewen Yu¹, Wenjin Hu²

¹School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China.

Journal of Imaging

|November 26, 2025

Summary

This study introduces VMT-Net, a novel image restoration network using Vision Mamba and Transformer architectures to enhance detail recovery in adverse weather. The network effectively mitigates semantic loss and blurring, improving image quality.

Keywords:

Vision-Mamba Transformer adaptive content guidance adverse weather image restoration

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Adverse weather conditions like snow and rain degrade image quality, causing global semantic loss and local detail blurring.
Existing image restoration methods struggle with spatial-semantic conflicts and information loss in challenging environments.

Purpose of the Study:

To develop an advanced image restoration network capable of addressing semantic loss, detail blurring, and spatial-semantic conflicts.
To improve the reconstruction quality of images captured under diverse adverse weather conditions.

Main Methods:

Proposed a Vision-Mamba-Transformer (VMT) module integrating Vision Mamba's long-range dependency modeling with Transformer's global contextual reasoning.
Introduced an Adaptive Content Guidance (ACG) module for effective inter-layer feature fusion using dynamic gating and spatial-channel attention.
Embedded VMT and ACG modules within a U-Net backbone for multi-scale feature modeling and cross-layer fusion.

Main Results:

VMT-Net achieved significant improvements on the Snow100K dataset, with PSNR gains of up to 0.89 dB and SSIM gains of 0.91%.
Demonstrated comparable performance to baselines on Outdoor-Rain and Raindrop datasets, with superior real-world detail recovery.
The proposed network exhibits robustness and strong detail restoration capabilities across various adverse weather scenarios.

Conclusions:

The integration of Mamba and Transformer architectures in VMT-Net effectively enhances image restoration under adverse weather.
The VMT and ACG modules contribute to mitigating information loss, reducing detail blurring, and improving semantic consistency.
VMT-Net offers a robust solution for high-quality image reconstruction in challenging environmental conditions.