Aiming at the problem that the image lacks sufficient clear edge after deblurring by existing methods, a phased image deblurring method based on edge guidance and feature fusion is proposed, and the deblurring task is divided into two stages to gradually remove blur. Firstly, the codec network with double cross integrated attention module (DCIAM) is used to learn the content features of images at different scales to realize the preliminary removal of blur. Secondly, an edge branch network (EBM) is constructed to extract image edge features. Thirdly, an edge-guided deblurring module (EGDM) is designed to couple the content and edge features of images at different resolutions. Finally, cascaded residual blocks and DCIAMs are used to achieve further remove of blur, and a self-calibrated attention fusion module (SCAFM) is introduced to enhance the feature expression. The experimental results demonstrate that the average peak signal-to-noise ratio and structural similarity of the proposed method reach 32.78 dB and 0.964, respectively, which are superior to other comparison methods. The proposed method can significantly improve the deblurring performance and make the image edge structure more complete after deblurring.