HLF-SASR: A High–Low Frequency Guided Structure-Aware Super-Resolution Network for Digital Rock Images

Abstract

High-resolution digital rock images provide the foundation for pore-scale reservoir characterization, seepage simulation, and multi-scale flow analysis. However, the inherent trade-off between field of view and spatial resolution in micro-computed tomography limits the acquisition of high-resolution images over macro-scale rock samples. Deep learning-based super-resolution offers a way to mitigate this limitation by learning mappings from low-resolution to high-resolution images, aiming to recover fine-scale pore–throat structures while preserving a large field of view. Yet existing super-resolution methods still struggle with the strong structural heterogeneity and frequency characteristics of rock images, often producing blurred pore boundaries or over-smoothed mineral textures. This paper proposes a high/low-frequency guided structure-aware super-resolution network (HLF-SASR), which combines a component-aware backbone with a stacked hourglass backbone with an atrous spatial pyramid pooling (ASPP)-based multi-scale context module, a high/low-frequency collaborative attention module, and a temperature-scaled multi-expert fusion strategy. Experiments on a public digital rock dataset using two-dimensional sandstone and carbonate slices, where 200 X 200 and 400 X 400 image pairs are used as low-resolution and high-resolution inputs for 2 X super-resolution, show that HLF-SASR consistently surpasses representative super-resolution models such as enhanced deep super-resolution network (EDSR), residual channel attention network (RCAN), and multi-attention super-resolution neural network (MASR) in terms of peak signal-to-noise ratio and structural similarity index. Visual comparisons further demonstrate that the proposed network produces clearer and more continuous pore–throat boundaries, better preserves narrow fractures and sharp-corner structures, and avoids over-sharpening mineral matrices or introducing pseudo-textures, thereby providing more reliable reconstructions for digital rock analysis.