Deep neural networks have achieved the most outstanding performance in compressed sensing magnetic resonance imaging (CS-MRI) reconstruction by learning the potential structures of images from a large number of training samples. However, the required data comprising hundreds of subjects are usually rare. In this article, we remedy this problem by transferring the easy-to-get deep Gaussian denoisers trained with natural images for artifact reduction in the iterative recovery process without the use of full-sampled MRI data. To this end, we first train a set of deep Gaussian networks with natural images and then incorporate them into a play-and-plug framework that is built by modifying the proximal gradient algorithm with the classical momentum strategy. Furthermore, a nonlocal denoiser is employed for efficiently removing the artifacts. It is found that the momentum strategy can make the statistical distribution of artifacts approximately be Gaussian, making the Gaussian denoisers available for CS-MRI. In the experiments, we verify the rationality of the established framework and show that our method consistently outperforms state-of-the-art methods.