• 论文
主办单位:煤炭科学研究总院有限公司、中国煤炭学会学术期刊工作委员会
基于Real−ESRGAN的岩石CT图像超分辨率重建
  • Title

    Super-resolution reconstruction of rock CT images based on Real-ESRGAN

  • 作者

    李刚张亚兵杨庆贺邹军鹏才天刘航赵艺鸣

  • Author

    LI Gang;ZHANG Yabing;YANG Qinghe;ZOU Junpeng;CAI Tian;LIU Hang;ZHAO Yiming

  • 单位

    辽宁工程技术大学矿业学院

  • Organization
    College of Mining, Liaoning Technical University
  • 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。

  • 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.

  • 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

  • KeyWords

    rock CT images;super-resolution reconstruction;generate adversarial networks;image processing;microscopic structure of rocks

  • 基金项目(Foundation)
    国家自然科学基金资助项目(52174077)。
  • DOI
  • 引用格式
    李刚,张亚兵,杨庆贺,等. 基于Real−ESRGAN的岩石CT图像超分辨率重建[J]. 工矿自动化,2023,49(11):84-91.
  • Citation
    LI Gang, ZHANG Yabing, YANG Qinghe, et al. Super-resolution reconstruction of rock CT images based on Real-ESRGAN[J]. Journal of Mine Automation,2023,49(11):84-91.
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