KAMSR: Kernel-Sharing with Adaptive Modulation for Single Rock CT Image Super-Resolution

High-quality rock CT reconstruction is essential for digital rock physics, yet recovering fine pore–fracture structures under limited spatial resolution remains challenging. This study proposes KAMSR, a Kernel-Sharing Adaptive Modulation Super-Resolution framework that integrates Kernel-Sharing Residual Blocks with Adaptive Modulation Layers (AML) to restore high-frequency details while suppressing artifacts at low parameter cost. Through partial Conv3×3 kernel sharing across cascaded residual-dense units, the default ×4 generator contains only 4.66 M parameters, approximately 3.6× smaller than typical RRDBNet-based SOTA models (≈16.7 M). Experiments on carbonate, coal, and sandstone CT datasets show that KAMSR outperforms competing super-resolution methods. Averaged over the three lithologies at ×4 upscaling, it achieves a PSNR of 31.43 dB, an SSIM of 0.934, an LPIPS of 0.187, and an NIQE of 3.04. The reconstructions exhibit sharper pore boundaries, clearer mineral textures, and fewer artifacts, providing a more reliable basis for downstream digital-rock analyses.