轻量模型DilatedLoc提升超分辨成像速度与精度
An Efficient Inference Frame for SMLM (Single-Molecule Localization Microscopy)
- 提出轻量网络DilatedLoc，参数少于100MB
- 推理速度提升50%，GPU利用率更高
- 适合实时超分辨显微成像应用
单分子定位显微镜（SMLM）突破衍射极限，实现亚细胞分辨率。传统SMLM分析依赖点扩散函数（PSF）拟合，限制了复杂PSF模型的应用。近年来，深度学习方法显著提升了SMLM算法性能，但推理速度慢、模型体积大，制约了实际应用。本文提出一种高效的模型部署框架，设计轻量级神经网络DilatedLoc，旨在提升图像重建质量与推理速度。相比主流网络模型，DilatedLoc将参数量压缩至100 MB以下，推理速度提升50%，并通过新型部署架构实现优异的GPU利用率，兼容多种网络模型。
原文摘要 · Abstract (English)
Single-molecule localization microscopy (SMLM) surpasses the diffraction limit, achieving subcellular resolution. Traditional SMLM analysis methods often rely on point spread function (PSF) model fitting, limiting the application of complex PSF models. In recent years, deep learning approaches have significantly improved SMLM algorithms, yielding promising results. However, limitations in inference speed and model size have restricted the widespread adoption of deep learning in practical applications. To address these challenges, this paper proposes an efficient model deployment framework and introduces a lightweight neural network, DilatedLoc, aimed at enhancing both image reconstruction quality and inference speed. Compared to leading network models, DilatedLoc reduces network parameters to under 100 MB and achieves a 50% improvement in inference speed, with superior GPU utilization through a novel deployment architecture compatible with various network models.
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