Laser Journal, Volume. 46, Issue 1, 165(2025)
Fluorescence microscopy images super-resolution reconstruction based on unsupervised deep learning
[1] [1] Dong C, Loy C C, He K, et al. Image super-resolution using deep convolutional networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 38(2): 295-307.
[6] [6] Zhang X, Zhang W, Guo S, et al. UnTDIP: Unsupervised neural network for DEM super-resolution integrating terrain knowledge and deep prior[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 122: 103430.
[7] [7] Wang H, Rivenson Y, Jin Y, et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy[J]. Nature methods, 2019, 16(1): 103-110.
[8] [8] Qiao C, Li D, Guo Y, et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy[J]. Nature methods, 2021, 18(2): 194-202.
[9] [9] Liao J, Qu J, Hao Y, et al. Deep-learning-based methods for super-resolution fluorescence microscopy[J]. Journal of Innovative Optical Health Sciences, 2023, 16(03): 2230016.
[10] [10] Shocher A, Cohen N, Irani M. “zero-shot” super-resolution using deep internal learning[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 3118-3126.
[11] [11] Liu H, Guo H, Liu X. UHA-CycleGAN: Unpaired hybrid attention network based on CycleGAN for terahertz image super- resolution[J]. IET Image Processing, 2023, 17(8): 2547-2559.
[12] [12] Chenyu Y, Guang L, Yi Z, et al. CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN - CIRCLE).[J]. IEEE transactions on medical imaging, 2020, 39(1): 188-203.
[13] [13] Dmitry U, Vedaldi A, Victor L. Deep image prior[J]. International Journal of Computer Vision, 2020, 128(7): 1867-1888.
[14] [14] Zhao W, Zhao S, Li L, et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy[J]. Nature biotechnology, 2022, 40(4): 606-617.
[15] [15] Ledig C, Theis L, Huszar F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, 2017: 4681-4690.
[16] [16] Mittal A, Soundararajan R, Bovik A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal processing letters, 2012, 20(3): 209-212.
[17] [17] Belharbi S, Whitford M K M, Hoang P, et al. SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution[J]. arXiv preprint arXiv:2406.09168, 2024.
Get Citation
Copy Citation Text
CHEN Dongyu, CHENG Yu. Fluorescence microscopy images super-resolution reconstruction based on unsupervised deep learning[J]. Laser Journal, 2025, 46(1): 165
Category:
Received: Aug. 19, 2024
Accepted: Apr. 17, 2025
Published Online: Apr. 17, 2025
The Author Email: CHENG Yu (chengyu@gdut.edu.cn)