ViSSR: Physics-Informed Hybrid Vision-LSTM/State-Space Super-Resolution for Digital Rock Microstructure Analysis
- 1School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing 163318, China
- 2Department of Geology, Northeast Petroleum University, Qinhuangdao 066004, China
Abstract
Digital Rock Physics (DRP) requires high-fidelity pore-scale imaging, yet micro-computed tomography (μ-CT) trades spatial resolution against field of view, and standard super-resolution (SR) often fails to preserve transport-controlling pore topology. We propose Vision-LSTM and State-Space Super-Resolution (ViSSR), a physics-informed hybrid residual SR network that couples Vision-LSTM (ViL) for local anisotropic texture with a Mamba-based 2D selective state-space module (SS2D) for linear-complexity global context, and restores high-frequency structure via scale-conditioned residuals under an explicit partial-volume and PSF degradation model. Because reconstruction errors propagate into permeability and flow analysis, we report the pixel metrics PSNR, SSIM, and LPIPS together with topology-aware scores comprising pore-size-distribution earth mover’s distance (PSD-EMD; one-dimensional Wasserstein-1), porosity, connectivity, spanning, and fractal dimension. On DeepRock-SR, ViSSR attains 32.08 dB PSNR, 0.746 SSIM, and 0.04 PSD-EMD at ×4, and preserves pore topology across sandstone, carbonate, and coal where perceptual baselines hallucinate or oversmooth. Zero-shot slice-stacked reconstruction on independently acquired dual-resolution carbonates from MRCCM, project DRP-362, further yields volumetric transport gains over bicubic upsampling under scanner-realized ×4 degradation.
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BibTeX
@article{liu2026vissr,
title = {{ViSSR: Physics-Informed Hybrid Vision-LSTM/State-Space Super-Resolution for Digital Rock Microstructure Analysis}},
author = {Yanchang Liu and Shuyi Liu and Weiyi Zhao and Hanning Li and Yiting Liao and Yifan Xu},
journal = {Applied Sciences},
year = {2026},
month = oct,
doi = {10.3390/app162010106},
url = {https://doi.org/10.3390/app162010106},
note = {CC BY},
}
RIS
TY - JOUR
TI - ViSSR: Physics-Informed Hybrid Vision-LSTM/State-Space Super-Resolution for Digital Rock Microstructure Analysis
AU - Yanchang Liu
AU - Shuyi Liu
AU - Weiyi Zhao
AU - Hanning Li
AU - Yiting Liao
AU - Yifan Xu
PY - 2026
DA - 2026/10/26
JO - Applied Sciences
DO - 10.3390/app162010106
UR - https://doi.org/10.3390/app162010106
AB - Digital Rock Physics (DRP) requires high-fidelity pore-scale imaging, yet micro-computed tomography (μ-CT) trades spatial resolution against field of view, and standard super-resolution (SR) often fails to preserve transport-controlling pore topology. We propose Vision-LSTM and State-Space Super-Resolution (ViSSR), a physics-informed hybrid residual SR network that couples Vision-LSTM (ViL) for local anisotropic texture with a Mamba-based 2D selective state-space module (SS2D) for linear-complexity global context, and restores high-frequency structure via scale-conditioned residuals under an explicit partial-volume and PSF degradation model. Because reconstruction errors propagate into permeability and flow analysis, we report the pixel metrics PSNR, SSIM, and LPIPS together with topology-aware scores comprising pore-size-distribution earth mover’s distance (PSD-EMD; one-dimensional Wasserstein-1), porosity, connectivity, spanning, and fractal dimension. On DeepRock-SR, ViSSR attains 32.08 dB PSNR, 0.746 SSIM, and 0.04 PSD-EMD at ×4, and preserves pore topology across sandstone, carbonate, and coal where perceptual baselines hallucinate or oversmooth. Zero-shot slice-stacked reconstruction on independently acquired dual-resolution carbonates from MRCCM, project DRP-362, further yields volumetric transport gains over bicubic upsampling under scanner-realized ×4 degradation.
SN - 2076-3417
ER -