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SFQMamba: A Spatial-Frequency Deraining Framework for Robust Visual Sensing in UAV-Assisted IoT Systems.

Letian Deng^1,2,3, Chunyu Meng^1,2,3, Yuhong Zhou^1,2,3

¹College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Xianyang 712100, China.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

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This summary is machine-generated.

SFQMamba, a novel dual-branch network, enhances single-image deraining by fusing spatial and frequency features. This method effectively removes rain streaks while preserving image details, benefiting both image restoration and object detection in UAV applications.

Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Existing single-image deraining methods struggle with long-range dependency modeling and frequency-domain information.
There's a need for advanced techniques to improve rain streak suppression and detail recovery.

Purpose of the Study:

To introduce SFQMamba, a dual-branch network for robust single-image deraining.
To enhance the modeling of spatial-frequency features for improved image restoration.

Main Methods:

SFQMamba employs a dual-branch architecture: a CNN branch with a Fused Enhance Block (FEB) for spatial-frequency fusion and a Mamba branch with a Spatial-Aware Selective Fusion Block (SASFB) for 2D extension.
Features are hierarchically aggregated through concatenation and pointwise convolution.

Keywords:

CNN Mamba UAV dual-branch architecture image deraining

Related Experiment Videos

Main Results:

SFQMamba achieves robust restoration on Rain13K and Raindrop datasets, outperforming TransMamba in PSNR and SSIM.
Demonstrates significant improvements in object detection (YOLOv8s) on the RainVisDrone benchmark, particularly on medium-rain subsets.

Conclusions:

The proposed SFQMamba framework effectively addresses limitations in current deraining methods.
It offers benefits for both low-level visual restoration and downstream object perception tasks in UAV scenarios.