MMamba: Enhancing image deraining with Morton curve-driven locality learning

Rain streaks in images exhibit complex forms and spatial structures. Their removal not only requires restoring the clarity of the image but also aims to preserve as much of the original information as possible. Transformer-based deep learning models have made significant progress in image deraining, effectively capturing the diversity of rain streaks and removing them through deep feature learning. However, when handling long sequence data, the computational complexity rises sharply, severely limiting the efficiency of practical applications. In recent years, the Mamba module, with its unique design, has demonstrated great potential in handling long sequences and enabling fast inference in image deraining tasks. It reduces computational complexity while preserving the underlying features of the image, thus improving efficiency. However, compared to Transformer-based models, the Mamba model still faces certain limitations in preserving local information and dealing with complex rain streaks. To address these issues, this paper proposes an efficient MMamba model, which combines the Morton curve-based state space model (SSM) to further enhance the ability to retain local information. Additionally, a dynamic channel attention mechanism is introduced, which significantly improves image detail restoration by partitioning channels and assigning learnable weights to each subset. Finally, for images with rich and complex rain streaks, a Selective Rain Stripe Compensator is proposed, effectively identifying and removing these intricate rain streaks. Extensive experiments on five commonly used benchmark datasets demonstrate that our method outperforms state-of-the-art techniques in terms of performance.