In practical inverse synthetic aperture radar (ISAR), the traditional imaging algorithms have low range and low cross-range resolutions while the echoes have limited bandwidth and sparse azimuth aperture in small coherent processing interval. To obtain super-resolution ISAR imaging at low signal-to-noise (SNR) ratios, this paper puts forward a novel fast two-dimensional joint linearized Bregman iteration (2D-JLBI) algorithm based on compressive sensing theory. Firstly, the radar echoes are established as a two-dimensional joint sparse representation model in the range frequency-azimuth Doppler domain. Consequently, the original two-dimensional super resolution imaging problem is converted into a two-dimensional jointly compressive reconstruction problem. Secondly, to avoid the reconstruction complexity from the vectorization of the echoes, the two-dimensional joint linearized Bregman iterative algorithm is proposed. Meanwhile, three strategies, namely the weighted residual iteration, estimation of the stagnation step, and optimizing the condition numbers of sensing matrices, are combined to improve the convergence speed. Both the ISAR imaging ability at low SNR and its speed are improved obviously. Finally, simulation experiments show that the proposed algorithm can shorten the imaging time and have better noise robustness under the condition of sub-Nyquist sampling rate and low SNR.