SkelEM: Explicit Decoupling of Topology and Details for Self-supervised Axial Super-Resolution in Volume Microscopy

Volume microscopy, including electron and light microscopy,suffers from severe anisotropic resolution due to physical axial section-ing. Existing self-supervised axial super-resolution (ASR) methods facea trilemma bounded by overly smoothed regression textures, structuralhallucinations of pure diffusion models, and prohibitive inference latency.In this paper, we propose Skeleton-refinE Microscopy (SkelEM), a self-supervised framework that decouples ASR at the training-signal level:a frozen topological network and a diffusion refiner are optimized bydisjoint objectives, separating low-frequency topology formulation fromhigh-frequency detail enhancement. Building on this deterministic skele-ton, we exploit a unified cycle-consistent mechanism on input sparseslices to simultaneously extract a real-domain residual prior and bidi-rectionally align the diffusion refiner, washing away cross-plane arti-facts without synthetic bias. By truncating the reverse diffusion processwith this physical prior, SkelEM achieves high-fidelity detail restorationin merely ≤ 5 steps. To rigorously assess cross-instrument generaliza-tion, we further introduce BRAVE-ASR, a new benchmark of co-alignedanisotropic and isotropic volumes acquired on a Plasma-FIB instrument.Across public benchmarks, SkelEM achieves the most favorable balanceacross the fidelity-perception trade-off among self-supervised methods,with state-of-the-art downstream membrane segmentation performanceand robust zero-shot generalization across distinct modalities.