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Academic Journal of Computing & Information Science, 2023, 6(12); doi: 10.25236/AJCIS.2023.061205.

Accelerated Single-Molecule Microscopy Image Reconstruction Algorithm Based on TensorRT


Qinfang Zhang

Corresponding Author:
Qinfang Zhang

School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, China


The effectiveness of deep learning-based high-density localization techniques in expediting applications of single-molecule localization microscopy is well-acknowledged. However, existing methods within this domain grapple with speed limitations, particularly when confronted with extensive raw data sets. To address this challenge, this study introduces a high-density localization approach founded on TensorRT, incorporating an enhanced U-shaped network for the swift reconstruction of raw images. Experimental verification conducted on a dataset featuring microtubule proteins illustrates that the proposed algorithm significantly amplifies the overall pace of reconstruction without compromising the precision of image reconstruction. This not only showcases a noteworthy advancement in speed but also establishes a practical technical remedy for the swift and high-fidelity reconstruction of fluorescence single-molecule microscopy images.


microscopy; fluorescence microscopy; super-resolution; image reconstruction; TensorRT

Cite This Paper

Qinfang Zhang. Accelerated Single-Molecule Microscopy Image Reconstruction Algorithm Based on TensorRT. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 12: 45-49. https://doi.org/10.25236/AJCIS.2023.061205.


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