Recently, multiscale and multistage image deblurring methods have encountered issues such as insufficient multiscale image feature extraction and loss of feature information due to stage deepening. To address the above problems, an image deblurring method based on an enhanced multiscale feature network is proposed in this paper. First, a multiscale residual feature extraction module is proposed, and convolution kernels with various sizes are used in the two branches to expand the receptive field and fully extract the feature information of images with various resolution sizes. Second, a cross-stage attention module is proposed to filter and transfer the key features of the image. Finally, a cross-stage feature fusion module, similar to a jump connection, is designed to compensate for feature loss and fuse feature information from input images with various sizes, to enrich spatial feature information, and to improve texture processing. Experimental results on the GoPro and HIDE datasets show that the proposed method can successfully reconstruct the image.