<p>In current image super-resolution reconstruction of digital rock images, most existing networks focus either on single-scale local convolutional features or on a single form of global attention modeling, while lacking the capability to jointly model multi-scale pore structures, high-frequency boundary textures, and long-range connectivity geometry. To address these challenges, this paper proposes a frequency–spatial collaborative enhanced super-resolution network, termed FSCNet, which enables fine-grained reconstruction of pore microstructures with a lightweight architecture. FSCNet incorporates four key innovative components, each targeting a specific challenge in digital rock reconstruction. First, the multi-scale dynamic convolution module (MSDC) is introduced to address the difficulty of modeling heterogeneous pore structures with different sizes, orientations, and spatial distributions, thereby improving the reconstruction of pore boundaries, bedding structures, and micro-fractures. Second, the multi-scale spatial attention (MSSA) module is designed to alleviate the insufficient emphasis on structurally important regions by enhancing pore edges, mineral interfaces, and texture connectivity areas. Third, the frequency–spatial bridge (FSB) module establishes interaction between the frequency and spatial domains to overcome the weak recovery of high-frequency information, improving restoration of pore boundaries and fine mineral textures. Finally, the adaptive expert blending (AEB) module mitigates the imbalance between local detail extraction and global structural modeling by adaptively fusing the local dynamic convolution branch and the sparse global attention branch according to lithological structures. Extensive experiments on the Carbonate2D and Sandstone2D digital rock datasets demonstrate that FSCNet achieves competitive PSNR and SSIM performance, validating its effectiveness for digital rock image super-resolution reconstruction.</p>