Based on plug and play (PnP) framework, a PnP 2D\|FISTA is proposed in this paper in combination with two dimensional fast iterative shrinkage thresholding algorithm (2D\|FISTA) and deep de\|noising network DnCNN for effective high\|resolution inverse synthetic aperture radar (ISAR) imaging on data with different signal\|to\|noise ratios. Firstly, models on signals and sparse observation of 2D ISAR imaging are established by this method, and iterative steps of the 2D\|FISTA are introduced. Then, being used as a de\|noiser, the trained deep de\|noising network DnCNN replaces the soft threshold shrinkage function, achievinggoodreconstruction and de\|noising performance. The experimental results show that PnP 2D\|FIST can achieveeffectively high\|resolution ISAR imaging with good reconstruction performance and noise\|robustness under different signal\|to\|noise ratio conditions.