Compressive sensing (CS) is proposed as a new signal compressive sampling theory in recent years. At the coding end CS obtains compressed data through projection, which requires more computing resources and higher implementation cost. Different from the standard compressed sensing, this paper proposes an image compression sampling method based on edge information assistance. In other words, some pixels of the image are randomly collected as measurement, and the pixels near the image edge are sampled with a high probability. Finally, the nonlinear optimization method is used to restore the image. The proposed sampling strategy obtains the random measurements and the adaptive measurements respectively through two steps. This paper gives the physical description of the sampling strategy and realizes it through simulation experiment. At the same time, the optimal ratio of edge information in sampling matrix is also discussed. Experimental results show that the proposed algorithm can quickly and effectively recover high quality images.