Anisotropic resolution remains a fundamental challenge in 3D microscopy, where axial resolution is significantly lower than lateral resolution due to physical limitations. To address this, we propose a self-supervised volume super-resolution (VSR) framework named Diffusion to Resolution (D2R), which leverages 2D diffusion priors to enhance axial resolution without requiring high-resolution (HR) volume as supervision. D2R consists of three stages: (1) learning biological priors via a 2D diffusion model trained on high-resolution XY slices, (2) generating pseudo-HR lateral (XZ/YZ) volumes through cross-plane fusion, and (3) performing stable structure distillation to train a 3D VSR network. To further improve VSR quality, we introduce Axial Enhancement Network (AENet), a 3D VSR model incorporating lightweight channel attention to enhance fine details while maintaining inter-slice continuity. Extensive experiments on FIB-SEM datasets demonstrate that D2R-AENet outperforms state-of-the-art self-supervised methods in both image similarity and membrane segmentation accuracy, achieving performance close to supervised approaches. These results validate the effectiveness of our framework in high-fidelity volumetric reconstruction under practical conditions where HR references are unavailable. Codes are available at https://github.com/hmzawz2/D2R-models .