Super-Resolution Carbonate Rock Image Beyond Instrument Limitations
Yang Meng, Kunning Tang, Heping Xie, Zhangxin Chen, Ying Teng, Yuntian Chen, Cunbao Li, Senyou AnSummary
Carbonate rocks, as complex multiscale porous media, present major imaging challenges because of intricate structures and strong heterogeneity. To address the trade-off between field of view (FOV) and resolution, we introduce the Swin transformer for image restoration generative adversarial network (SwinIRGAN), a super-resolution (SR) framework based on sliding-window attention that captures long-range features efficiently. The model balances global consistency with high-frequency detail preservation and learns the mapping between low-resolution (LR) and high-resolution (HR) images. Using a biogenic carbonate data set, SwinIRGAN achieves 99.48% accuracy in Euler’s number and 97.05% accuracy in higher-resolution extrapolation. For the multiresolution complex carbonates micro-computed tomography (micro-CT, MRCCM) data set, the proposed reconstruction and extrapolation workflow improves Euler’s number by 15.59% compared with the baseline. Results show that SwinIRGAN preserves mineralogical and topological characteristics across scales and provides more reliable digital rocks for pore-scale analysis and flow simulation.