Multi-stage network for single image deblurring based on dual-domain window mamba

Multi-stage methods have been proven effective and widely used in image deblurring research. These methods, usually designed based on Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs), have limitations, including the inability to capture global contextual information and a quadratic increase in computational complexity as image resolution. Additionally, although current methods have incorporated frequency domain information, they do not sufficiently explore the interrelationships of different frequencies. To address these issues, we proposed a Multi-Stage Visual Dual-Domain Window Mamba (DDWMamba) approach to realize image deblurring, leveraging the benefits of state space models (SSMs) for image data. First, to achieve better deblurring effects, we used a multi-stage design approach in which each stage maintains the details and global information of the original resolution image. Second, we proposed a DDWMamba Block, which includes a Spatial Window Visual Mamba and a Frequency Window Visual Mamba, aiming to fully explore the correlations between different pixels in both the spatial and frequency domains. Finally, to implement a coarse-to-fine design approach in the multi-stage method and reduce model complexity, we set a window operation with different window sizes for each stage. DDWMamba is extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to existing state-of-the-art deblurring methods.