Defocus deblurring is a challenging task in the fields of computer vision and image processing. The irregularity of defocus blur kernels, coupled with the limitations of computational resources, poses significant difficulties for defocused image restoration. Additionally, the varying degrees of blur across different regions of the image impose higher demands on feature capture. Insufficient fine-grained feature extraction can result in artifacts and the loss of details, while inadequate coarse-grained feature extraction can cause image distortion and unnatural transitions. To address these challenges, we propose a defocus image deblurring method based on a hybrid CNN–Mamba architecture. This approach employs a data-driven, network-based self-learning strategy for blur processing, eliminating the need for traditional blur kernel estimation. Furthermore, by designing parallel feature extraction modules, the method leverages the local feature extraction capabilities of CNNs to capture image details, effectively restoring texture and edge information. The Mamba module models long-range dependencies, ensuring global consistency in the image. On the real defocus blur dual-pixel image dataset DPDD, the proposed CMDDNet achieves a PSNR of 28.37 in the Indoor dataset, surpassing Uformer-B (28.23) while significantly reducing the parameter count to only 9.74 M, which is 80.9% less than Uformer-B (50.88 M). Although the PSNR on the Outdoor and Combined datasets is slightly lower, CMDDNet maintains competitive performance with a significantly reduced model size, demonstrating its efficiency and effectiveness in defocus deblurring. These results indicate that CMDDNet offers a promising trade-off between performance and computational efficiency, making it well suited for lightweight applications.