Due to atmospheric turbulence and system noise , the images of astronomical or space objects are blurred and degraded. The dual channel alternating minimization algorithm is one of the effective methods for restoring images degraded by turbulent and noise. However , this algorithm is relatively complex and requires repeated iterative operations , resulting in a longer processing time. In order to improve the algorithm running speed , the graphics proces- sor ( GPU) acceleration technology based on the algorithm structure features is applied to the dual channel alternating minimization algorithm , with a focus on optimizing the iterative process of alternating minimization. The experimental results show that under the condition of different atmospheric turbulence and Signal-to -noise ratio 20 dB ,compared with the algorithm directly using the central processing unit ( CPU) , GPU parallel acceleration for the dual channel al- ternating minimization algorithm can achieve the " U-step" operation rate of image restoration increased by more than 80% , and the " H-step" operation rate of point spread function solution increased by more than 60% , and the recon- structed images are close to the diffraction limit. The combination of parallel acceleration technology and existing algo- rithms can effectively improve the running speed , providing a certain reference for the restoration of degraded images caused by turbulence and noise.