A dynamic scene deblurring algorithm based on multi-scale dense connections and U-Net improvement is proposed to address the issues of texture detail loss, inability to suppress noise, and generation of ringing artifacts in existing motion blur removal networks during image restoration. First, the receptive field is effectively expanded by using the hole convolution downsampling in the U-Net network to avoid irreversible damage to the image without increasing the number of parameters, and the sub-pixel convolution is used to obtain clear image details with a small convolution kernel in the upsampling process, reducing the computational complexity; Secondly, a multi-scale dense feature extraction (MDFE) module is designed to enhance deep level feature extraction and reuse through densely connected convolutional layers. Spatial pyramid pooling (SPP) branches are used to guide the transfer and fusion of multi-scale features, promoting effective preservation of image details and textures; Finally, the ConvLSTM bidirectional connectivity structure is used to compensate for contextual features of simple cascading loss from the encoding path in a nonlinear manner, promoting cross stage interaction of deep features, and weakening edge artifacts and noise interference. Compared with existing advanced methods, the performance advantages of the algorithm proposed in this paper have been verified.