Atmospheric turbulence causes image degradation. For a single degraded image of atmospheric turbulence, an image restoration method based on grid networks was proposed in this study. To realize local and deep multiscale feature extraction, dilated convolution was used in the backbone module to expand the model sensory field. Additionally, a spatial attention module was added to the post-processing module. This enabled to better deal with the white spots and artifacts in the restored image and improve image quality. Experimental results show that the proposed network quickly outputs recovery results, demonstrating an average restoration output time of 0.29 s, and the average peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the simulated data obtained using the proposed algorithm in a dynamic scene are maximally improved up to 9.44 dB and 0.1173, respectively, compared with other methods. Furthermore, the algorithm exhibits better effect for recovering atmospheric turbulence in real scenes.