Super-Resolution Carbonate Rock Image Beyond Instrument Limitations

Summary 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.