Super resolution reconstruction of μ-CT image of rock sample using neighbour embedding algorithm

被引:39
|
作者
Wang, Yuzhu [1 ]
Rahman, Sheik S. [1 ]
Arns, Christoph H. [1 ]
机构
[1] Univ New South Wales, Sch Petr Engn, Sydney, NSW, Australia
关键词
mu-CT; Super resolution; Neighbour embedding; Self-similarity; POROUS-MEDIA; STOCHASTIC CHARACTERIZATION; SUPERRESOLUTION; PERMEABILITY; MULTISCALE; SIMULATION;
D O I
10.1016/j.physa.2017.10.022
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
X-ray computed tomography (mu-CF) is considered to be the most effective way to obtain the inner structure of rock sample without destructions. However, its limited resolution hampers its ability to probe sub-micro structures which is critical for flow transportation of rock sample. In this study, we propose an innovative methodology to improve the resolution of mu-CT image using neighbour embedding algorithm where low frequency information is provided mu-CT image itself while high frequency information is supplemented by high resolution scanning electron microscopy (SEM) image. In order to obtain prior for reconstruction, a large number of image patch pairs contain high- and low-image patches are extracted from the Gaussian image pyramid generated by SEM image. These image patch pairs contain abundant information about tomographic evolution of local porous structures under different resolution spaces. Relying on the assumption of self-similarity of porous structure, this prior information can be used to supervise the reconstruction of high resolution mu-CT image effectively. The experimental results show that the proposed method is able to achieve the state-of-the-art performance. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:177 / 188
页数:12
相关论文
共 50 条
  • [1] An Overview of Image Super-resolution Reconstruction Algorithm
    Niu, Xiaoming
    2018 11TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2, 2018, : 16 - 18
  • [2] Image reconstruction with improved super-resolution algorithm
    Chen, CY
    Kuo, YC
    Fuh, CS
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2004, 18 (08) : 1513 - 1527
  • [3] An Improved Neighbor Embedding Method to Super-resolution Reconstruction of a Single Image
    Yu, Wen-Sen
    Chen, Shu-qing
    CEIS 2011, 2011, 15
  • [4] Edge-Enhanced Super-Resolution Reconstruction of Rock CT Images
    Gao, Chennian
    Qiu, Chen
    PATTERN RECOGNITION AND COMPUTER VISION, PT IX, PRCV 2024, 2025, 15039 : 276 - 289
  • [5] Image super resolution reconstruction algorithm based on weighted random forest
    Wu C.-D.
    Lu Z.-W.
    Yu X.-S.
    Kongzhi yu Juece/Control and Decision, 2019, 34 (10): : 2243 - 2248
  • [6] Image super-resolution reconstruction algorithm based on channel shuffle
    Wang, Li
    He, Dongzhi
    2021 ASIA-PACIFIC CONFERENCE ON COMMUNICATIONS TECHNOLOGY AND COMPUTER SCIENCE (ACCTCS 2021), 2021, : 225 - 229
  • [7] Image super-resolution reconstruction algorithm based on fractional calculus
    Lei J.
    Wang H.
    Zhu L.
    Xiao J.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2017, 39 (12): : 2849 - 2856
  • [8] Super-resolution Reconstruction Algorithm Using Vibro-imaging and Correlation Image Sensor
    Fujimori, Naotsuna
    Ando, Shigeru
    2012 PROCEEDINGS OF SICE ANNUAL CONFERENCE (SICE), 2012, : 2028 - 2032
  • [9] Image Super Resolution Using Expansion Move Algorithm
    Zhang, Dong-Xiao
    Cai, Guo-Rong
    Liang, Zong-Qi
    Huang, Huan
    QUANTITATIVE LOGIC AND SOFT COMPUTING 2016, 2017, 510 : 641 - 657
  • [10] Spatial Super Resolution Based Image Reconstruction using HIBP
    Nayak, Rajashree
    Monalisa, S.
    Patra, Dipti
    2013 ANNUAL IEEE INDIA CONFERENCE (INDICON), 2013,