Images captured on rainy days are prone to rain streaking on various scales. These images taken on a rainy day will be disturbed by rain streaks of varying degrees, resulting in degradation of image quality. This study sought to eliminate rain streaks from images using a two-stage network architecture involving progressive multi-scale recovery and aggregation. The proposed multi-scale aggregation residual channel attention fusion network (MARCAFNet) uses kernels of various scales to recover details at various levels of granularity to enhance the robustness of the model to streaks of various sizes, densities, and shapes. When applied to benchmark datasets, the proposed method outperformed other state-of-the-art schemes in the restoration of image details without distorting the image structure.

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