@Article{cmes.2025.073640,
AUTHOR = {Tiantian Wang, Zhihua Hu},
TITLE = {Efficient Image Deraining through a Stage-Wise Dual-Residual Network with Cross-Dimensional Spatial Attention},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {145},
YEAR = {2025},
NUMBER = {2},
PAGES = {2357--2381},
URL = {http://www.techscience.com/CMES/v145n2/64599},
ISSN = {1526-1506},
ABSTRACT = {Rain streaks introduced by atmospheric precipitation significantly degrade image quality and impair the reliability of high-level vision tasks. We present a novel image deraining framework built on a three-stage dual-residual architecture that progressively restores rain-degraded content while preserving fine structural details. Each stage begins with a multi-scale feature extractor and a channel attention module that adaptively emphasizes informative representations for rain removal. The core restoration is achieved via enhanced dual-residual blocks, which stabilize training and mitigate feature degradation across layers. To further refine representations, we integrate cross-dimensional spatial attention supervised by ground-truth guidance, ensuring that only high-quality features propagate to subsequent stages. Inter-stage feature fusion modules are employed to aggregate complementary information, reinforcing reconstruction continuity and consistency. Extensive experiments on five benchmark datasets (Rain100H, Rain100L, RainKITTI2012, RainKITTI2015, and JRSRD) demonstrate that our method establishes new state-of-the-art results in both fidelity and perceptual quality, effectively removing rain streaks while preserving natural textures and structural integrity.},
DOI = {10.32604/cmes.2025.073640}
}