A Multi-Stage Dual Encoder-Decoder Network Based on Event Image Cross-Modal Fusion for Image Deblurring

Most existing event-driven image deblurring methods ignore inherent differences between the two modalities and lack explicit alignment strategies, leading to cross-modal mismatches and degraded feature reconstruction. To address this issue, a multi-stage dual encoder-decoder image deblurring method based on event image cross-modal fusion is proposed. The proposed network consists of an encoder and a decoder. The encoder employs dilated convolutional residual modules for feature extraction. It also integrates a cross-modal feature fusion module and a local scoring mechanism. These components combine event features with frame image features while suppressing noise. The decoder reconstructs image features via two directional decoding sub-networks. It also incorporates a feedback attention module. This module selects informative features along the feedback path. As a result, the image reconstruction quality is enhanced. In addition to the standard loss, mean absolute error, structural similarity, and frequency reconstruction losses are used to optimize deblurring performance. Extensive experiments are conducted on the GoPro, REBlur, and RwEvent datasets. For PSNR, our method exceeds REFID by 0.23 dB, 0.20 dB, and 0.49 dB on the three datasets. For SSIM, our model achieves gains of 0.002, 0.002, and 0.017 against REFID. In terms of computational cost and inference speed, our network adds only 3.4 M parameters and 105.2 GFLOPs, with an FPS reduction of only 2.12. This trivial efficiency loss delivers significant improvements in both pixel and structural restoration performance. Ablation experiments verify the independent positive contribution of each designed module. Both qualitative visual comparisons and quantitative metrics demonstrate that the proposed network has stronger deblurring and generalization capabilities.