  Visual-based measurement systems are frequently affected by rainy weather due
to the degradation caused by rain streaks in captured images, and existing
imaging devices struggle to address this issue in real-time. While most efforts
leverage deep networks for image deraining and have made progress, their large
parameter sizes hinder deployment on resource-constrained devices.
Additionally, these data-driven models often produce deterministic results,
without considering their inherent epistemic uncertainty, which can lead to
undesired reconstruction errors. Well-calibrated uncertainty can help alleviate
prediction errors and assist measurement devices in mitigating risks and
improving usability. Therefore, we propose an Uncertainty-Driven Multi-Scale
Feature Fusion Network (UMFFNet) that learns the probability mapping
distribution between paired images to estimate uncertainty. Specifically, we
introduce an uncertainty feature fusion block (UFFB) that utilizes uncertainty
information to dynamically enhance acquired features and focus on blurry
regions obscured by rain streaks, reducing prediction errors. In addition, to
further boost the performance of UMFFNet, we fused feature information from
multiple scales to guide the network for efficient collaborative rain removal.
Extensive experiments demonstrate that UMFFNet achieves significant performance
improvements with few parameters, surpassing state-of-the-art image deraining
methods.
