In traditional secret image-sharing schemes, all the data of a secret image has to be processed, which prolongs the algorithm execution. Meanwhile, the inflated data become a burden for network transmission and disk storage when many secret images need to be routinely shared. Compressed sensing technology measures the original image perceptually through a proper measurement matrix, and the measured data cover the vast majority of the useful information of the original image. While ensuring precise reconstruction, the original image is compressed from high dimensional to low dimensional, and the amount of image data decreases dramatically. Thus, a number of problems caused by the large amount of data in traditional secret image-sharing schemes could be solved by compressed sensing. In this paper, we combine traditional secret image-sharing with compressed sensing technology and show, through experiments, that the proposed method can clearly reduce the amount of data that needs to be processed and effectively shorten the algorithm execution time. The experimental results reveal that our method can shorten the image-sharing time by 2.7% to 57.3% and the image restoration time by 3.3% to 57.7% under different compression ratios.