Deep learning-driven methods for fluorescence imaging denoising

Abstract

Fluorescence imaging, serving as the primary imaging modality in modern life science research, faces a fundamental challenge in achieving high-sensitivity imaging: optimizing the signal-to-noise ratio (SNR) under dynamic and complex experimental conditions. Due to autofluorescence, shot noise, and tissue scattering, this SNR deficiency disrupts subcellular morphometry, restricts recording reliability, and ultimately propagates artifacts in subsequent analysis. This review evaluates data-driven deep learning denoising methods that overcome conventional limitations through effective feature extraction and nonlinear modeling. Focusing on fluorescence imaging acquisition under photon-limited conditions, we delineate cutting-edge architectures, including supervised learning, unsupervised learning, zero-shot learning, and hybrid approaches. By producing higher-fidelity image data, these denoising methods enhance the reliability of live-cell imaging and the accuracy of neural mechanism analysis. This advancement provides a stronger foundation for elucidating dynamic biological processes and accelerating precision medicine.

Keywords

deep learning / fluorescence imaging / image denoising

References

[1] H. Wu, Y. Zhao, X. Zhou, T. Wu, J. Qian, S. Wu, Y. Liu, C. Zuo, Adv. Imag. 2024, 1, 032001.

[2] Z. Zhang, L. Guo, L. Yao, Y. Li, Y. Hei, Q. Wang, X. Wang, R. Ma, X. Yang, Z. Hu, W. Wu, Interdiscip. Med. 2025, 3, e20240048.

[3] D. Hassan, J. Domínguez, B. Midtvedt, H. Klein Moberg, J. Pineda, C. Langhammer, G. Volpe, A. Homs Corbera, C. B. Adiels, Adv. Photonics 2024, 6, 064001.

[4] W. Li, Z. Huang, C. Zhou, X. Zhang, W. Fan, D. Liang, Z. Hu, Interdiscip. Med. 2023, 1, e20230012.

[5] S. Li, Q. Chen, C. Kim, S. Choi, Y. Wang, Y. Zhang, C. Li, Photoacoustics 2025, 43, 100723.

[6] B. Lin, F. Xing, L. Su, K. Wang, Y. Liu, D. Zhang, X. Yang, H. Tan, Z. Zhu, D. Wang, Light Sci. Appl. 2025, 14, 178.

[7] A. Rehman, A. Zhovmer, R. Sato, Y. S. Mukouyama, J. Chen, A. Rissone, R. Puertollano, J. Liu, H. D. Vishwasrao, H. Shroff, C. A. Combs, H. Xue, Sci. Rep. 2024, 14, 18184.

[8] J. Liu, X. Huang, L. Chen, S. Tan, Opt. Express 2020, 28, 14859.

[9] D. Xu, Y. Gu, J. Lu, L. Xu, W. Wang, B. Dong, Nanoscale 2024, 16, 5729.

[10] S. Salwig, J. Drefs, J. Lücke, PLoS Comput. Biol. 2024, 20, e1012192.

[11] J. Lequyer, R. Philip, A. Sharma, W.-H. Hsu, L. Pelletier, Nat. Mach. Intell. 2022, 4, 953.

[12] N. Wagner, F. Beuttenmueller, N. Norlin, J. Gierten, J. C. Boffi, J. Wittbrodt, M. Weigert, L. Hufnagel, R. Prevedel, A. Kreshuk, Nat. Methods 2021, 18, 557.

[13] B. Mandracchia, X. Hua, C. Guo, J. Son, T. Urner, S. Jia, Nat. Commun. 2020, 11, 94.

[14] X. Huang, J. Fan, L. Li, H. Liu, R. Wu, Y. Wu, L. Wei, H. Mao, A. Lal, P. Xi, L. Tang, Y. Zhang, Y. Liu, S. Tan, L. Chen, Nat. Biotechnol. 2018, 36, 451.

[15] Z. H. Shah, M. Müller, W. Hübner, T. C. Wang, D. Telman, T. Huser, W. Schenck, GigaScience 2024, 13, giad109.

[16] J. Chen, H. Sasaki, H. Lai, Y. Su, J. Liu, Y. Wu, A. Zhovmer, C. A. Combs, I. Rey-Suarez, H. Y. Chang, C. C. Huang, X. Li, M. Guo, S. Nizambad, A. Upadhyaya, S. J. J. Lee, L. A. G. Lucas, H. Shroff, Nat. Methods 2021, 18, 678.

[17] Y. Zhang, Y. Zhu, E. Nichols, Q. Wang, S. Zhang, C. Smith, S. Howard, in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), IEEE, Long Beach, CA, USA 2019, pp. 11710–11718.

[18] S. Chaudhary, S. Moon, H. Lu, Nat. Commun. 2022, 13, 5165.

[19] Y. Miao, Y. Cheng, Y. Xia, Y. Hei, W. Wang, Q. Dai, J. Suo, C. Chen, Nat. Commun. 2025, 16, 74.

[20] X. Li, Y. Li, Y. Zhou, J. Wu, Z. Zhao, J. Fan, F. Deng, Z. Wu, G. Xiao, J. He, Y. Zhang, G. Zhang, X. Hu, X. Chen, Y. Zhang, H. Qiao, H. Xie, Y. Li, H. Wang, L. Fang, Q. Dai, Nat. Biotechnol. 2023, 41, 282.

[21] J. Lehtinen, J. Munkberg, J. Hasselgren, S. Laine, T. Karras, M. Aittala, T. Aila, (Preprint) arXiv, 1803.04189, v3, 2018.

[22] A. Krull, T.-O. Buchholz, F. Jug, in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), IEEE, Long Beach, CA, USA 2019, pp. 2129–2137.

[23] Y. Quan, M. Chen, T. Pang, H. Ji, in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), IEEE, Seattle, WA, USA 2020, pp. 1890–1898.

[24] B. Manifold, E. Thomas, A. T. Francis, A. H. Hill, D. Fu, Biomed. Opt. Express 2019, 10, 3860.

[25] J. Lecoq, M. Oliver, J. H. Siegle, N. Orlova, P. Ledochowitsch, C. Koch, Nat. Methods 2021, 18, 1401.

[26] L. Qu, S. Zhao, Y. Huang, X. Ye, K. Wang, Y. Liu, X. Liu, H. Mao, G. Hu, W. Chen, C. Guo, J. He, J. Tan, H. Li, L. Chen, W. Zhao, Nat. Methods 2024, 21, 1895.

[27] C. Broaddus, A. Krull, M. Weigert, U. Schmidt, G. Myers, in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), IEEE, Iowa City, IA, USA 2020, pp. 159–163.

[28] X. Li, X. Hu, X. Chen, J. Fan, Z. Zhao, J. Wu, H. Wang, Q. Dai, Nat. Comput. Sci. 2023, 3, 1067.

[29] G. Zhang, X. Li, Y. Zhang, X. Han, X. Li, J. Yu, B. Liu, J. Wu, L. Yu, Q. Dai, Nat. Methods 2023, 20, 1957.

[30] M. Eom, S. Han, P. Park, G. Kim, E. S. Cho, J. Sim, K. H. Lee, S. Kim, H. Tian, U. L. Böhm, E. Lowet, H. A. Tseng, J. Choi, S. E. Lucia, S. H. Ryu, M. Rózsa, S. Chang, P. Kim, X. Han, K. D. Piatkevich, M. Choi, C. H. Kim, A. E. Cohen, J. B. Chang, Y. G. Yoon, Nat. Methods 2023, 20, 1581.

[31] X. Li, G. Zhang, J. Wu, Y. Zhang, Z. Zhao, X. Lin, H. Qiao, H. Xie, H. Wang, L. Fang, Q. Dai, Nat. Methods 2021, 18, 1395.

[32] F. Zhang, R. Huang, M. Xin, H. Meng, D. Gao, Y. Fu, J. Gao, X. Ji, (Preprint) bioRxiv, https://doi.org/10.1101/2025.04.08.645087, 2025.

[33] W. Lee, M. A. Jang, H. S. Nam, J. Song, J. Choi, J. W. Song, J. Y. Seok, P. Kim, J. W. Kim, H. Yoo, PhotoniX 2025, 6, 15.

[34] H. Chen, J. Najera, D. Geresu, M. Datta, C. Smith, S. Howard, in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), IEEE, Nashville, TN, USA 2025, pp. 4758–4767.

[35] C. Qiao, Y. Zeng, Q. Meng, X. Chen, H. Chen, T. Jiang, R. Wei, J. Guo, W. Fu, H. Lu, D. Li, Y. Wang, H. Qiao, J. Wu, D. Li, Q. Dai, Nat. Commun. 2024, 15, 4180.

[36] Q. Ma, J. Jiang, X. Zhou, P. Liang, X. Liu, J. Ma, IEEE Trans. Pattern Anal. Mach. Intell. 2025, 47, 4614.

[37] L. Xiao, C. Fang, L. Zhu, Y. Wang, T. Yu, Y. Zhao, D. Zhu, P. Fei, Opt. Express 2020, 28, 30234.

[38] Z. H. Shah, M. Müller, T. C. Wang, P. M. Scheidig, A. Schneider, M. Schüttpelz, T. Huser, W. Schenck, Photonics Res. 2021, 9, B168.

[39] X. Chen, C. Qiao, T. Jiang, J. Liu, Q. Meng, Y. Zeng, H. Chen, H. Qiao, D. Li, J. Wu, PhotoniX 2024, 5, 4.

[40] B. Dai, S. You, K. Wang, Y. Long, J. Chen, N. Upreti, J. Peng, L. Zheng, C. Chang, T. J. Huang, Y. Guan, S. Zhuang, D. Zhang, Sci. Adv. 2025, 11, eadq2494.

[41] M. E. Xie, Y. Adam, L. Z. Fan, U. L. Böhm, I. Kinsella, D. Zhou, M. Rozsa, A. Singh, K. Svoboda, L. Paninski, A. E. Cohen, Cell Rep. 2021, 35, 108954.

[42] J. L. Lillvis, H. Otsuna, X. Ding, I. Pisarev, T. Kawase, J. Colonell, K. Rokicki, C. Goina, R. Gao, A. Hu, K. Wang, J. Bogovic, D. E. Milkie, L. Meienberg, B. D. Mensh, E. S. Boyden, S. Saalfeld, P. W. Tillberg, B. J. Dickson, eLife 2022, 11, e81248.

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2026 The Author(s). Interdisciplinary Medicine published by Wiley-VCH GmbH on behalf of Nanfang Hospital, Southern Medical University.