Multiscale modeling algorithm for core images.

Computed tomography (CT) images of large core samples acquired by imaging equipment are insufficiently clear and ineffectively describe the tiny pore structure; conversely, images of small core samples are insufficiently globally representative. To alleviate these challenges, the idea of a super-resolution reconstruction algorithm is combined with that of a three-dimensional core reconstruction algorithm, and a multiscale core CT image fusion reconstruction algorithm is proposed. To obtain sufficient image quality with high resolution, a large-scale core image is used to provide global feature information as well as information regarding the basic morphological structure of a large-scale pore and particle. Then the texture pattern and the tiny pore distribution information of a small-scale core image is used to refine the coarse large-scale core image. A blind image quality assessment is utilized to estimate the degradation model of core images at different scales. A multilevel pattern mapping dictionary containing local binary patterns is designed to speed up the pattern matching procedure, and an adaptive weighted reconstruction algorithm is designed to reduce the blockiness. With our method, images of the same core at different scales were successfully fused. The proposed algorithm is extensively tested on microstructures of different rock samples; all cases of the reconstructed results and those of the actual sample were found to be in good agreement with each other. The final reconstructed image contains both large-scale and small-scale information that can provide a better understanding of the core samples and inform the accurate calculation of parameters.

[1]  Pejman Tahmasebi,et al.  HYPPS: A hybrid geostatistical modeling algorithm for subsurface modeling , 2017 .

[2]  Jun Zhang,et al.  Continuous rotation invariant local descriptors for texton dictionary-based texture classification , 2013, Comput. Vis. Image Underst..

[3]  Kenneth Stuart Sorbie,et al.  Representation of multiscale heterogeneity via multiscale pore networks , 2013 .

[4]  Marina V Karsanina,et al.  Hierarchical Optimization: Fast and Robust Multiscale Stochastic Reconstructions with Rescaled Correlation Functions. , 2018, Physical review letters.

[5]  D. Ruderman.  The statistics of natural images , 1994 .

[6]  Lizhong Xu,et al.  Object tracking based on support vector dictionary learning , 2018, Signal Image Video Process..

[7]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[8]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[9]  Sabine Süsstrunk,et al.  Registration of aliased images for super-resolution imaging , 2006, Electronic Imaging.

[10]  Pejman Tahmasebi,et al.  Nanoscale and multiresolution models for shale samples , 2018 .

[11]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[12]  Shutao Li,et al.  Multi-morphology image super-resolution via sparse representation , 2013, Neurocomputing.

[13]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[14]  Mingliang Liang,et al.  Evolution of pore structure in gas shale related to structural deformation , 2017 .

[15]  Mehdi Ostadhassan,et al.  Nanoscale pore structure characterization of the Bakken shale in the USA , 2017 .

[16]  Dirk Mallants,et al.  Universal Stochastic Multiscale Image Fusion: An Example Application for Shale Rock , 2015, Scientific Reports.

[17]  Wen Gao,et al.  Low-Rank Decomposition-Based Restoration of Compressed Images via Adaptive Noise Estimation , 2016, IEEE Transactions on Image Processing.

[18]  Truong Q. Nguyen,et al.  Deep Wide-Activated Residual Network Based Joint Blocking and Color Bleeding Artifacts Reduction for 4:2:0 JPEG-Compressed Images , 2019, IEEE Signal Processing Letters.

[19]  D. Ruderman,et al.  Statistics of cone responses to natural images: implications for visual coding , 1998 .

[20]  Tao Li,et al.  Rotation expanded dictionary-based single image super-resolution , 2016, Neurocomputing.

[21]  M. E. Naraghi,et al.  A stochastic permeability model for the shale-gas systems , 2015 .

[22]  Lei Zhang,et al.  A Feature-Enriched Completely Blind Image Quality Evaluator , 2015, IEEE Transactions on Image Processing.

[23]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[24]  Farzam Javadpour,et al.  Multiscale, Multiphysics Network Modeling of Shale Matrix Gas Flows , 2013, Transport in Porous Media.

[25]  Sabine Süsstrunk,et al.  A Frequency Domain Approach to Registration of Aliased Images with Application to Super-resolution , 2006, EURASIP J. Adv. Signal Process..