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 is used to learn the content features of images at different scales to realize the preliminary removal of blur. Secondly, an edge branch network is constructed to extract image edge features. Thirdly, an edge-guided deblurring module is designed to couple the content and edge features of images at different resolutions. Finally, cascaded residual blocks and dual cross integrated attention modules are used to achieve further remove of blur, and a self-calibrated attention fusion module 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 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.