Super-resolution of real-world rock microcomputed tomography images using cycle-consistent generative adversarial networks

Chen, Honggang, Xiaohai, He, Teng, Qizhi, Sheriff, Ray E. ORCID: 0000-0003-4143-692X, Feng, Junxi and Xiong, Shuhua (2020) Super-resolution of real-world rock microcomputed tomography images using cycle-consistent generative adversarial networks. Physical Review E, 101 (2). 023305. ISSN 2470-0045

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Abstract

Digital rock imaging plays an important role in studying the microstructure and macroscopic properties of rocks, where microcomputed tomography (MCT) is widely used. Due to the inherent limitations of MCT, a balance should be made between the field of view (FOV) and resolution of rock MCT images—a large FOV at low resolution (LR) or a small FOV at high resolution (HR). However, large FOV and HR are both expected for reliable analysis results in practice. Super-resolution (SR) is an effective solution to break through the mutual restriction between the FOV and resolution of rock MCT images, for it can reconstruct an HR image from a LR observation. Most of the existing SR methods cannot produce satisfactory HR results on real-world rock MCT images. One of the main reasons for this is that paired images are usually needed to learn the relationship between LR and HR rock images. However, it is challenging to collect such a dataset in a real scenario. Meanwhile, the simulated datasets may be unable to accurately reflect the model in actual applications. To address these problems, we propose a cycle-consistent generative adversarial network (CycleGAN)-based SR approach for real-world rock MCT images, namely, SRCycleGAN. In the off-line training phase, a set of unpaired rock MCT images is used to train the proposed SRCycleGAN, which can model the mapping between rock MCT images at different resolutions. In the on-line testing phase, the resolution of the LR input is enhanced via the learned mapping by SRCycleGAN. Experimental results show that the proposed SRCycleGAN can greatly improve the quality of simulated and real-world rock MCT images. The HR images reconstructed by SRCycleGAN show good agreement with the targets in terms of both the visual quality and the statistical parameters, including the porosity, the local porosity distribution, the two-point correlation function, the lineal-path function, the two-point cluster function, the chord-length distribution function, and the pore size distribution. Large FOV and HR rock MCT images can be obtained with the help of SRCycleGAN. Hence, this work makes it possible to generate HR rock MCT images that exceed the limitations of imaging systems on FOV and resolution.

Item Type: Article
Additional Information: ©2020 American Physical Society
Uncontrolled Keywords: Artificial neural networks, Porous media, Imaging & optical processing, X-ray imaging
Subjects: Q Science > QE Geology
T Technology > TN Mining engineering. Metallurgy
Divisions: School of Engineering
Depositing User: Professor Ray Sheriff
Date Deposited: 21 Apr 2020 14:32
Last Modified: 21 Apr 2020 14:32
Identification Number: 10.1103/PhysRevE.101.023305
URI: http://ubir.bolton.ac.uk/id/eprint/2762

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