MPINet: Multi-Stage Progressive Illumination-Aware Network for Image Deraining

Image deraining remains a critical challenge in computer vision, as rain streaks significantly degrade visual quality by introducing complex patterns that overlay scene content, impeding applications from autonomous driving to surveillance systems. Existing methods often struggle to balance global scene understanding with precise removal of rain streaks, resulting in either residual rain artifacts or over-smoothed textures. To address this, we propose MPINet, a Multi-Stage Progressive Illumination-Aware Network that integrates illumination awareness and global-context modeling, which is specifically designed for rain-removal tasks. Our architecture features a novel illumination-aware module that generates illumination maps to enhance robustness in the varying lighting conditions commonly encountered during rainy scenarios. The UniMetaFormer core adaptively incorporates global semantic information through dynamic transformations and attention mechanisms, effectively distinguishing between rain streaks and underlying image content. Inspired by MPRNet’s multi-stage restoration framework, our network employs a hierarchical approach with progressive patch-based processing and deep supervision across three stages, enabling efficient integration of our illumination-aware modules while maintaining reasonable model complexity. Experimental results verify the effectiveness of MPINet for rain-removal tasks, demonstrating superior capability in removing rain streaks of varying densities while preserving original image textures. On average, MPINet outperforms MPRNet by approximately 6.5% in terms of PSNR and 1.3% in terms of SSIM across all datasets.