In　this　work,　we　presents　a　super-resolution　(SR)　reconstruction　method　for　the　synthetic　aperture　radar　(SAR)　images　based　on　the　generative　adversarial　network　(GAN),　SRGAN　for　short.　In　comparison　with　conventional　SR　algorithms　developed　in　the　area　of　image　processing,　the　proposed　SRGAN　technique　could　make　an　important　breakthrough　in　terms　of　reconstruction　accuracy　and　computational　efficiency　for　the　SAR　image　SR.　To　achieve　high-resolution,　high　fidelity　and　optics　photo-like　SAR　images,　SRGAN　explores　a　perceptual　loss　function　consisting　of　an　adversarial　loss　and　a　content　loss.　Selected　experimental　results　based　on　Terra-SAR　datasets　are　provided　to　demonstrate　the　state-of-the-art　performance　of　our　proposed　method.