基于Real−ESRGAN 的岩石CT 图像超分辨率重建李刚， 张亚兵， 杨庆贺， 邹军鹏， 才天， 刘航， 赵艺鸣（辽宁工程技术大学 矿业学院，辽宁 阜新 123000）摘要：图像采集设备和地质环境等因素导致岩石CT 图像分辨率低、细节不清晰，而现有图像超分辨率重建方法在表征内部高密度矿物质颗粒和孔裂隙时容易丢失细节。
针对上述问题，采用改进的增强型超分辨率生成对抗网络（Real−ESRGAN ）对岩石CT 图像进行超分辨率重建。
选取山西晋城无烟煤矿业集团有限责任公司赵庄煤矿15号煤层底板的砂岩为研究对象，研究不同图像放大倍数下Real−ESRGAN 的重建性能，并将其与超分辨率卷积神经网络（SRCNN ）、超分辨率生成对抗网络（SRGAN ）、增强型超分辨率生成对抗网络（ESRGAN ）、增强的深度超分辨率网络（EDSR ）等算法进行对比。
试验结果表明：① 使用Real−ESRGAN 重建的高分辨率图像在视觉效果上比原始CT 图像更清晰，重建图像中裂隙轮廓和高密度矿物质颗粒更加突出，图像可视性得到了极大提高。
② 在客观评估方面，Real−ESRGAN 算法在2倍超分辨率重建后图像的峰值信噪比（PSNR ）高达36.880 dB ，结构相似性（SSIM ）达0.933。
但随着放大倍数的增加，6倍超分辨率重建图像上的孔隙出现模糊，PSNR 降至32.781 dB ，SSIM 为0.896。
③ Real−ESRGAN 重建超分辨图像的孔隙率和喉道长度分布占比与原始CT 图像相比非常接近，保留了岩石重要的细观结构信息。
关键词：岩石CT 图像；超分辨率重建；生成对抗网络；图像处理；岩石细观结构中图分类号：TD67 文献标志码：ASuper-resolution reconstruction of rock CT images based on Real-ESRGANLI Gang, ZHANG Yabing, YANG Qinghe, ZOU Junpeng, CAI Tian, LIU Hang, ZHAO Yiming(College of Mining, Liaoning Technical University, Fuxin 123000, China)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收稿日期：2023-08-26；修回日期：2023-11-16；责任编辑：盛男。
