Rainy weather weak target recognition over water: an enhanced de-raining approach for QSNet networks

To address rainwater interference in visual imaging systems for water navigation, exploration, and rescue by proposing an improved QSMD network based on QSNet. To mitigate channel information loss, we combine guided astrous convolution with CBAM, enhancing the model's spatial representation and feature expression while optimizing computational efficiency and maintaining high accuracy. Experimental results confirm the effectiveness of this improvement.