Abstract
We present a Physics-guided deep learning framework to address common limitations in Confocal Laser Scanning Microscopy (CLSM), including diffraction-limited resolution, noise, and under sampling due to low laser power conditions. The optical system’s point spread function and primary CLSM image degradation mechanisms, namely photon shot noise, dark current noise, motion blur, speckle noise, and under sampling are explicitly incorporated into the model as physics-based constraints. A convolutional autoencoder is trained with a custom loss function that integrates these optical degradation processes, ensuring that the reconstructed images adhere to physical image formation principles. The model is evaluated on simulated CLSM datasets generated based on experimentally observed CLSM noise characteristics. Statistical comparisons, including intensity histograms, spatial frequency distributions, and structural similarity metrics, confirm that the synthetic dataset closely matches accurate CLSM data. The proposed approach is compared with traditional image reconstruction methods, including Richardson-Lucy deconvolution, non-negative least squares, and total variation regularization. Results indicate that the physics-constrained autoencoder improves structural detail recovery while maintaining consistency with known CLSM imaging physics. This study demonstrates that Physics-guided deep learning can provide an alternative computational approach to CLSM enhancement, complementing existing optical correction methods. Future work will focus on further validation using experimental CLSM acquisitions.
Data availability
The datasets used in this paper are available upon reasonable request from the corresponding author. Specific details about data sources and preprocessing steps are described in the Materials and Methods section.
References
McLeod, E. & Ozcan, A. Unconventional methods of imaging: computational microscopy and compact implementations. Rep. Prog. Phys. 79 (7), 076001 (2016).
Fischer, R. S., Wu, Y., Kanchanawong, P., Shroff, H. & Waterman, C. M. Microscopy in 3D: a biologist’s toolbox. Trends Cell Biol. 21 (12), 682–691 (2011).
Jerkovic, I. & Cavalli, G. Understanding 3D genome organization by multidisciplinary methods. Nat. Rev. Mol. Cell Biol. 22 (8), 511–528 (2021).
Cutrale, F., Fraser, S. E. & Trinh, L. A. Imaging, visualization, and computation in developmental biology. Annual Rev. Biomedical Data Sci. 2 (1), 223–251 (2019).
Stender, A. S. et al. Single cell optical imaging and spectroscopy. Chem. Rev. 113 (4), 2469–2527 (2013).
Buchberger, A. R., DeLaney, K., Johnson, J. & Li, L. Mass spectrometry imaging: a review of emerging advancements and future insights. Anal. Chem. 90 (1), 240 (2017).
Hauser, M. et al. Correlative super-resolution microscopy: new dimensions and new opportunities. Chem. Rev. 117 (11), 7428–7456 (2017).
Shah, S., Crawshaw, J. & Boek, E. Three-dimensional imaging of porous media using confocal laser scanning microscopy. J. Microsc. 265 (2), 261–271 (2017).
Zhivov, A., Stachs, O., Stave, J. & Guthoff, R. F. In vivo three-dimensional confocal laser scanning microscopy of corneal surface and epithelium. Br. J. Ophthalmol. 93 (5), 667–672 (2009).
Scivetti, M., Pilolli, G. P., Corsalini, M., Lucchese, A. & Favia, G. Confocal laser scanning microscopy of human cementocytes: analysis of three-dimensional image reconstruction. Annals Anatomy-Anatomischer Anzeiger. 189 (2), 169–174 (2007).
Jones, C. W., Smolinski, D., Keogh, A., Kirk, T. & Zheng, M. Confocal laser scanning microscopy in orthopaedic research. Prog. Histochem. Cytochem. 40 (1), 1–71 (2005).
Liu, S., Weaver, D. L. & Taatjes, D. J. Three-dimensional reconstruction by confocal laser scanning microscopy in routine pathologic specimens of benign and malignant lesions of the human breast. Histochem. Cell Biol. 107 (4), 267–278 (1997).
Wright, S. J. et al. Introduction to confocal microscopy and three-dimensional reconstruction. Methods Cell. Biol. 38, 1–45 (1993).
Teng, X., Li, F. & Lu, C. Visualization of materials using the confocal laser scanning microscopy technique. Chem. Soc. Rev. 49 (8), 2408–2425 (2020).
Braat, J. J., van Haver, S., Janssen, A. J. & Dirksen, P. Assessment of optical systems by means of point-spread functions. Progress Opt. 51, 349–468 (2008).
Rossmann, K. Point spread-function, line spread-function, and modulation transfer function: tools for the study of imaging systems. Radiology 93 (2), 257–272 (1969).
Xie, X., Chen, Y., Yang, K. & Zhou, J. Harnessing the point-spread function for high-resolution far-field optical microscopy. Phys. Rev. Lett. 113 (26), 263901 (2014).
Stallinga, S. & Rieger, B. Accuracy of the Gaussian point spread function model in 2D localization microscopy. Opt. Express. 18 (24), 24461–24476 (2010).
Karataev, P. et al. First observation of the point spread function of optical transition radiation. Phys. Rev. Lett. 107 (17), 174801 (2011).
Su, J., Xu, B. & Yin, H. A survey of deep learning approaches to image restoration. Neurocomputing 487, 46–65 (2022).
Tai, Y., Yang, J., Liu, X. & Xu, C. (eds) Memnet: A persistent memory network for image restoration. In Proceedings of the IEEE international conference on computer vision (2017).
Ali, S. et al. A deep learning framework for quality assessment and restoration in video endoscopy. Med. Image. Anal. 68, 101900 (2021).
Wang, G., Ye, J. C. & De Man, B. Deep learning for tomographic image reconstruction. Nat. Mach. Intell. 2 (12), 737–748 (2020).
Born, M. & Wolf, E. Principles of Optics: Electromagnetic Theory of Propagation (Elsevier, 2013).
Richards, B. & Wolf, E. Electromagnetic diffraction in optical systems, II. Structure of the image field in an aplanatic system. Proc. Royal Soc. Lond. Ser. Math. Phys. Sci. 253 (1274), 358–379 (1959).
Acknowledgements
The authors declare that no external funding was received for this work.
Author information
Authors and Affiliations
Contributions
Z.A. conceived the idea, generated the simulated CLSM datasets, designed and implemented the deep learning model, performed training and data analysis, and wrote the original draft. J.S., and A.H., assisted in developing and scripting the deep learning network and contributed to writing. U.S., and T.Q. contributed to data analysis and manuscript writing. A.S., Z.E.K., S.M., S.P., O.A.R., and B.A supported data analysis, validation, and manuscript revision. W.M. supervised the project as principal investigator and contributed to manuscript writing and revision. All authors reviewed and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Ahmad, Z., Shabeer, J., Hidayat, A. et al. Enhanced confocal microscopy with physics-guided autoencoders via synthetic noise modeling. Sci Rep (2026). https://doi.org/10.1038/s41598-025-34839-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-025-34839-x
