Traditional radar imaging methods suffer from the problems of low resolution and poor noise suppression. We propose a new radar imaging method based on Self-supervised deep-learning-assisted compressed sensing (SS-DL-CS-Net). The original radar image is used as the inputs of network. The network is trained to learn the mapping function between the original radar image and the high quality radar image. However, the high quality radar image can’t be obtained. We solve this problem by used the sparsity of radar image. The original radar image and image with the zeros value is used as the reference of network. Ours network don’t need data in advance to train. Real radar data are used to evaluate the performance of the proposed method. The experimental results demonstrate the superiority of the proposed method