Compressed sensing is an effective approach for fast magnetic resonance imaging (CSMRI) that employs sparsity to reconstruct MR images from undersampled k-space data. Synthesis and analysis sparse models are two representative directions. This work aims to develop an enhanced ADMM-Net on the basis of SADN model, which unifies synthesis and analysis prior by means of the convolutional operator. The present SADN-Net not only promotes the generative sparse feature maps to be sparse, but also enforces the convolution between the filter and trained images to be sparse. Besides, it uses optimized parameters learned from the training data. Experiments show that the proposed algorithm achieves higher reconstruction accuracies.