Abstract: Due to factors such as image acquisition equipment and geological environment, rock CT images have low resolution and unclear details. However, existing image super-resolution reconstruction methods are prone to losing details when characterizing high-density mineral particles and pores and cracks inside. To solve the above problems, an improved enhanced super-resolution generative adversarial network (Real-ESRGAN) is used for super-resolution reconstruction of rock CT images. The sandstone of the 15th coal seam floor in Zhaozhuang Coal Mine, Shanxi Jincheng Anthracite Mining Group Co., Ltd. is selected as the research object to study the reconstruction performance of Real-ESRGAN under different image magnifications. It is compared with algorithms such as super-resolution convolutional neural network (SRCNN), super-resolution generative adversarial network (SRGAN), enhanced super-resolution generative adversarial network (ESRGAN), and enhanced deep super-resolution network (EDSR). The experimental results show the following points. ① The high-resolution images reconstructed using Real-ESRGAN have clearer visual effects than the original CT images. The contours of cracks and high-density mineral particles in the reconstructed images are more prominent, greatly improving the visibility of the images. ② In terms of objective evaluation, the Real-ESRGAN algorithm achieves a peak signal-to-noise ratio (PSNR) of 36.880 dB and a structural similarity (SSIM) of 0.933 in the image after 2x super-resolution reconstruction. But as the magnification increases, the pores on the 6x super-resolution reconstructed image become blurry, with PSNR decreasing to 32.781 dB and SSIM reaching 0.896. ③ The porosity and throat length distribution ratio of the Real-ESRGAN reconstructed super-resolution image are very close to the original CT image, preserving important microstructural information of the rock.