In order to simulate heterogeneous distribution in microscopic displacement experiment, it is necessary to splice several different pore binary images into a complete image. However, these spliced images do not have overlapping areas, so image splicing and restoration should be carried out according to the texture information of the image to be spliced. This paper studies the correlation of image neighborhood and proposes an image Mosaic method based on block matching and multi-level sampling. This method combines five decision criteria to repair curved contours and irregular textures between pore image boundaries. Through the image Mosaic process from coarse to fine scale, The final spliced thin section pore image can reflect the core characteristics more truly. In order to verify the effectiveness of this research method, the proposed algorithm is compared with existing traditional image repair algorithms and image repair methods based on deep learning, and the results of image Mosaic examples are evaluated by subjective visual and objective indicators. The results show that the proposed algorithm is superior to the existing image restoration algorithms in PSNR and SSIM, improving 6.08dB and 0.015 respectively, and has better performance in natural texture transition and overall structure consistency.