A Lightweight Transformer-Based Network for Image Deraining with Feature-Wise Attention and Cross-Level Feature Refinement

The aim in single-image deraining tasks is to remove rain streaks from degraded images while preserving scene structures and fine details. However, existing deep learning-based methods often face a trade-off between restoration quality and computational efficiency, and many models struggle to capture hierarchical information effectively under complex rain conditions. To address these limitations, we propose a lightweight cross-gated hierarchical transformer for image deraining. The proposed network adopts a five-stage encoder–decoder architecture with Multi-head Feature-wise Attention (MFA) to efficiently model channel-wise dependencies while reducing the computational burden associated with conventional self-attention. In addition, an Enhanced Gated Depthwise Feed-Forward Network (EGDFN) is introduced to obtain refined feature representations with improved efficiency, and a Cross-Level Feature Refinement (CLFR) module is designed to enhance information exchange between corresponding encoder and decoder stages, thereby strengthening hierarchical feature integration and preserving structural details. The network is trained using a single SSIM-based loss, which enhances the structural fidelity of the restored results. Extensive experiments on four synthetic datasets, two real-world datasets, and a downstream semantic segmentation benchmark demonstrate that the proposed method consistently achieves strong restoration performance, producing cleaner outputs with sharper details and improved effectiveness for subsequent vision tasks.