关键词: 图像去模糊, 动态运动模糊, 注意力机制, 多尺度特征提取, 频率-空间域融合

Abstract: Recently, encoder-decoder-based architectures have made significant strides in high-resolution image deblurring tasks. However, for complex dynamic motion blur in high-resolution scenarios, existing methods are mostly limited to single-domain feature extraction or shallow cross-domain concatenation, failing to deeply integrate the structural consistency of the spatial domain with the detail recovery capability of the frequency domain. Moreover, these methods exhibit limitations in receptive field design, which restricts multi-scale feature extraction and leads to a bottleneck in perceiving complex, heterogeneous blur patterns. To address these limitations, a Cross-domain multi-scale Deblurring Network (CDNet) was proposed to jointly model spatial and frequency-domain representations. Firstly, a Cross-Domain Gated Attention (CDGA) module that integrates spatial and frequency domains was designed to extract cross-domain features. Then, a dynamic cross-domain weighting mechanism was equipped for the module to achieve adaptive feature fusion and selectively enhance blur-related cues. Finally, a Multi-Scale Feed-Forward Network (MSFFN) was designed to capture contextual information across different receptive fields, thereby strengthening the integration of local and global blur features. Extensive experimental results on three public datasets, GoPro, HIDE, and RealBlur, demonstrate that compared with the baseline model Restormer (Restoration Transformer), the proposed method improves the Peak Signal-to-Noise Ratio (PSNR) by 0.46 dB, 0.29 dB, and 0.04 dB, and improves the Structural Similarity Index Measure (SSIM) index by 0.007, 0.019, and 0.02, respectively. The experimental results indicate that the proposed method outperforms existing state-of-the-art methods across multiple metrics, exhibiting superior deblurring performance and the ability to restore fine-grained image details more effectively.

Key words: image deblurring, dynamic motion blur, attention mechanism, multi-scale feature extraction, spatial-frequency feature fusion