Aiming at the problems of inaccurate feature extraction and insufficient use of effective features in existing dynamic scene image deblurring methods, this paper proposes a dynamic scene image deblurring network based on two-branch feature extraction and cyclic refinement. The whole network consists of feature extraction network, cyclic refinement network (CRN) and image reconstruction (IR). Among them, the feature extraction network includes the extraction of detail and contour features (CFs) of the blurred image, using the residual unit as the basic unit of the feature extraction network. The cyclic refinement network refines the feature map by alternately fusing contour features and detail features (DFs) to obtain the refinement features (RFs) of the blurred image. Finally, in the image reconstruction stage, the contour and detail features are reused and combined with the residual learning strategy to fuse the contour features, detail features and refined features step by step, and then the clear image is reconstructed by nonlinear mapping. The experimental results on the widely used dynamic scene blurring dataset GOPRO show that the average peak signal to noise ratio (PSNR) of this method reaches 31.86, and the average structure similarity (SSIM) reaches 0.947 3. The images restored by the proposed method contain rich details and have better deblurring effect. The proposed method is superior to the comparison method in terms of objective evaluation index and subjective visual effect.