The resolution and imaging quality of super-resolution fluorescence imaging significantly depend on the number of fluorescent molecular photons collected during the experiment, as well as the background noise. To obtain fast super-resolution fluorescence microscopy imaging under low photon count and high background light conditions, the proposed convolutional neural network is employed to restore the signal with extremely low signal-to-noise ratio (SNR) and combined with the reconstruction network to perform super-resolution imaging. The results show that the fluorescence signal can be effectively recovered under the condition of low signal-to-noise ratio, the peak signal-to-noise ratio can reach 27 dB, which is significantly better than the other two algorithms. The proposed method can also cooperate with Deep-STORM reconstruction network to obtain fast super-resolution imaging under low SNR conditions. The normalized mean square error of the reconstructed result is 7.5%, and the resolution is significantly improved compared to the other similar algorithms. Additionally, the reconstruction results under experimental conditions verify the ability of the proposed method and provide a feasible solution for fast super-resolution fluorescence imaging under weak signals.