  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 as the input of net. The net is trained to learn the
mapping function between the original radar image and the high quality radar
image. However, the high quality radar image cant be obtained. We solve this
problem by used the sparsity of radar image. The original radar image and image
with the zeros value as the reference of net. Ours net dont need a lot of data
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
