At present, U-Net-based image deblurring algorithms have some problems, such as detail loss and poor image quality. Therefore, the U-Net structure is improved，and an image deblurring method based on multi-scale optimization and dynamic feature fusion is proposed in this paper. Firstly, according to detail loss, a simplified and effective MSRM is proposed to extract finer image features by increasing feature scale diversity. In addition, in order to transfer more favorable features to the decoding part, a dynamic feature fusion module is designed at the skip connection, which can selectively fuse different stages of encoding features by attention weighting. In this algorithm, multi-scale content loss and multi-scale high-frequency information loss are used for constraint training. Experimental results on GoPro and RealBlur data sets show that the proposed method can effectively improve image quality and restore more detailed information. Compared with the existing deblurring algorithms, the proposed algorithm has certain advantages in subjective vision and objective evaluation.