A unified reconstruction algorithm for reduced-frame structured illumination microscopy

Jingxiang Zhang; Tianyu Zhao; Manming Shu; Keru Mou; Zheming Zhang; Yihan Sun; Mengrui Wang; Yansheng Liang; Shaowei Wang; Ming Lei

arXiv · 2608.12964

A unified reconstruction algorithm for reduced-frame structured illumination microscopy

Jingxiang Zhang·Tianyu Zhao·Manming Shu·Keru Mou·Zheming Zhang·Yihan Sun·Mengrui Wang·Yansheng Liang·Shaowei Wang·Ming Lei

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Abstract

Reduced-frame structured illumination microscopy (SIM) is attractive for live-cell imaging because it can improve temporal throughput and reduce photobleaching, but incomplete phase sampling makes reconstruction unstable and computationally demanding. Here we present URA-SIM, a unified reduced-acquisition framework that turns fixed reduced-frame measurements into pipeline- compatible raw stacks through model-consistent phase-domain completion. Instead of solving a large object-level inverse problem or replacing established SIM reconstruction, URA-SIM estimates the missing phase content on the low-dimensional phase-harmonic manifold required by the target modality and then delegates order separation and image formation to classical reconstruction pipeline. This design combines three practical advantages: fidelity from the SIM forward structure, lightweight online computation, and direct compatibility with existing reconstruction workflows. For 2D-SIM, URA-SIM uses the first-harmonic phase structure of three-phase SIM to estimate a shared zero-order field and complete missing phase samples by direction-wise harmonic fitting. On calibration and biological 2D-SIM data, reduced-frame reconstructions preserve resolvable structures and remain competitive on COS7 mitochondria comparison data. In live-cell COS7 mitochondria imaging, URA-SIM reconstructs each time point from five acquired raw frames and resolves mitochondrial cristae across different temporal sampling regimes. Experiments on 3D-SIM and nonlinear SIM further show that the same design principle can be transferred when the phase model and reconstruction-pipeline interface are adapted to the target modality. These results support URA-SIM as a transparent, model-consistent and computationally lightweight route from fixed reduced-frame acquisition to classical SIM reconstruction workflows.

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Jingxiang Zhang, Tianyu Zhao, Manming Shu, Keru Mou, Zheming Zhang, Yihan Sun, Mengrui Wang, Yansheng Liang, Shaowei Wang, Ming Lei. 2026-08-13. A unified reconstruction algorithm for reduced-frame structured illumination microscopy. https://arxiv.org/abs/2608.12964

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