Enhancing Resolution of Digital Rock Images with Super Resolution Convolutional Neural Networks

Abstract Single Image Super Resolution (SISR) techniques based on Super Resolution Convolutional Neural Networks (SRCNN) are applied to micro-computed tomography (micro-CT) images of sandstone and carbonate rocks. Digital rock imaging is limited by the capability of the scanning device resulting in a trade-off between resolution and field of view, and super resolution methods tested in this study aim to compensate for this limitation. SRCNN models SR-Resnet, Enhanced Deep SR (EDSR), and Wide-Activation Deep SR (WDSR) are used on the Digital Rock Super Resolution 1 (DRSRD1) Dataset of 4× downsampled images, comprising of 2000 high resolution (800 × 800) raw micro-CT images of Bentheimer sandstone and Estaillades carbonate. The trained models are applied to the validation and test data within the dataset and show a 3–5 dB rise (50%–70% reduction in relative error) in image quality compared to bicubic interpolation, with all tested models performing within a 0.1 dB range. Difference maps indicate that edge sharpness is completely recovered in images within the scope of the trained model, with only high frequency noise related detail loss. We find that aside from generation of high-resolution images, a beneficial side effect of super resolution methods applied to synthetically downgraded images is the removal of image noise while recovering edgewise sharpness which is important for the segmentation process. The model is also tested against low-resolution images of Bentheimer rock with image augmentation to account for natural noise and blur. The SRCNN method is shown to act as a preconditioner for image segmentation under these circumstances which naturally leads to further future development and training of models that segment an image directly. Image restoration by SRCNN on the rock images is of significantly higher quality than traditional methods and suggests SRCNN methods are a viable processing step in a digital rock workflow.

[1]  Yukai Wang,et al.  CT-image Super Resolution Using 3D Convolutional Neural Network , 2018, Comput. Geosci..

[2]  Ning Xu,et al.  Wide Activation for Efficient and Accurate Image Super-Resolution , 2018, ArXiv.

[3]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Hongyu Wang,et al.  End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks , 2016, IEEE Access.

[5]  B. Flannery,et al.  Three-Dimensional X-ray Microtomography , 1987, Science.

[6]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  J. Coenen,et al.  MEASUREMENT PARAMETERS AND RESOLUTION ASPECTS OF MICRO X-RAY TOMOGRAPHY FOR ADVANCED CORE ANALYSIS , 2004 .

[8]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[9]  Tim Salimans,et al.  Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks , 2016, NIPS.

[10]  Randy D. Hazlett,et al.  Simulation of capillary-dominated displacements in microtomographic images of reservoir rocks , 1995 .

[11]  Thomas S. Huang,et al.  Image super-resolution as sparse representation of raw image patches , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Martin J. Blunt,et al.  Computations of Absolute Permeability on Micro-CT Images , 2012, Mathematical Geosciences.

[13]  W. B. Lindquist,et al.  Medial axis analysis of void structure in three-dimensional tomographic images of porous media , 1996 .

[14]  Eirikur Agustsson,et al.  NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[15]  Dorthe Wildenschild,et al.  Image processing of multiphase images obtained via X‐ray microtomography: A review , 2014 .

[16]  P. Krakowska,et al.  Computed X-ray microtomography as the useful tool in petrophysics: A case study of tight carbonates Modryn formation from Poland , 2016 .

[17]  Peyman Mostaghimi,et al.  Computations of permeability of large rock images by dual grid domain decomposition , 2019, Advances in Water Resources.

[18]  Peyman Mostaghimi,et al.  Pore-scale simulation of dissolution-induced variations in rock mechanical properties , 2017 .

[19]  D. Wildenschild,et al.  X-ray imaging and analysis techniques for quantifying pore-scale structure and processes in subsurface porous medium systems , 2013 .

[20]  Xiaohai He,et al.  Sparse representation-based volumetric super-resolution algorithm for 3D CT images of reservoir rocks , 2017 .

[21]  Junko Ota,et al.  Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT , 2018, Journal of Digital Imaging.

[22]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[24]  Kyoung Mu Lee,et al.  Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).