[1] Y. Guo, D. Li, S. Zhang. Visualizing intracellular organelle and cytoskeletal interactions at nanoscale resolution on millisecond timescales. Cell, 175, 1430-1442.e17(2018).

[2] R. Li, M. Zhu, J. Li. Precise segmentation of densely interweaving neuron clusters using G-Cut. Nat. Commun., 10, 1549(2019).

[3] D. M. Chow, D. Sinefeld, K. E. Kolkman. Deep three-photon imaging of the brain in intact adult zebrafish. Nat. Methods, 17, 605-608(2020).

[4] K. Podgorski, G. Ranganathan. Brain heating induced by near-infrared lasers during multiphoton microscopy. J. Neurophysiol., 116, 1012-1023(2016).

[5] T. Wang, C. Wu, D. G. Ouzounov. Quantitative analysis of 1300-nm three-photon calcium imaging in the mouse brain. Elife, 9, e53205(2020).

[6] P. P. Laissue, R. A. Alghamdi, P. Tomancak. Assessing phototoxicity in live fluorescence imaging. Nat. Methods, 14, 657-661(2017).

[7] H. Dana, Y. Sun, B. Mohar. High-performance calcium sensors for imaging activity in neuronal populations and microcompartments. Nat. Methods, 16, 649-657(2019).

[8] N. Samantaray, I. Ruo-Berchera, A. Meda. Realization of the first sub-shot-noise wide field microscope. Light Sci. Appl., 6, e17005(2017).

[9] M. A. Taylor, W. P. Bowen. Quantum metrology and its application in biology. Phys. Rep., 615, 1-59(2016).

[10] C. A. Casacio, L. S. Madsen, A. Terrasson. Quantum-enhanced nonlinear microscopy. Nature, 594, 201-206(2021).

[11] W. Meiniel, J.-C. Olivo-Marin, E. D. Angelini. Denoising of microscopy images: a review of the state-of-the-art, and a new sparsity-based method. IEEE Trans. Image Process., 27, 3842-3856(2018).

[12] S. R. Schultz, C. S. Copeland, A. J. Foust. Advances in two-photon scanning and scanless microscopy technologies for functional neural circuit imaging. Proc. IEEE, 105, 139-157(2016).

[13] M. Jing, Y. Li, J. Zeng. An optimized acetylcholine sensor for monitoring in vivo cholinergic activity. Nat. Methods, 17, 1139-1146(2020).

[14] C. Xu. An adaptive excitation source for multiphoton deep and fast imaging. Proc. SPIE, 11971, 1197102(2022).

[15] J.-L. Starck, E. J. Candès, D. L. Donoho. The curvelet transform for image denoising. IEEE Trans. Image Process., 11, 670-684(2002).

[16] K. Dabov, A. Foi, V. Katkovnik. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process., 16, 2080-2095(2007).

[17] U. Schmidt, S. Roth. Shrinkage fields for effective image restoration. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2774-2781(2014).

[18] W. Aili, Z. Ye, M. Shaoliang. Image denoising method based on curvelet transform. 2008 3rd IEEE Conference on Industrial Electronics and Applications, 571-574(2008).

[19] A. Abdelhamed, S. Lin, M. S. Brown. A high-quality denoising dataset for smartphone cameras. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1692-1700(2018).

[20] M. Aittala, F. Durand. Burst image deblurring using permutation invariant convolutional neural networks. Proceedings of the European Conference on Computer Vision (ECCV), 731-747(2018).

[21] B. Du, Q. Wei, R. Liu. An improved quantum-behaved particle swarm optimization for endmember extraction. IEEE Trans. Geosci. Remote Sens., 57, 6003-6017(2019).

[22] R. Jaroensri, C. Biscarrat, M. Aittala. Generating training data for denoising real RGB images via camera pipeline simulation. arXiv(2019).

[23] X. Mao, C. Shen, Y.-B. Yang. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Proceedings of the 30th International Conference on Neural Information Processing Systems, 2810-2818(2016).

[24] M. Weigert, U. Schmidt, T. Boothe. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods, 15, 1090-1097(2018).

[25] C. Belthangady, L. A. Royer. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Methods, 16, 1215-1225(2019).

[26] L. Fang, F. Monroe, S. W. Novak. Deep learning-based point-scanning super-resolution imaging. Nat. Methods, 18, 406-416(2021).

[27] H. Wang, Y. Rivenson, Y. Jin. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods, 16, 103-110(2019).

[28] J. Lehtinen, J. Munkberg, J. Hasselgren. Noise2Noise: learning image restoration without clean data. arXiv(2018).

[29] A. Krull, T.-O. Buchholz, F. Jug. Noise2Void-learning denoising from single noisy images. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2129-2137(2019).

[30] J. Batson, L. Royer. Noise2Self: Blind denoising by self-supervision. International Conference on Machine Learning, 524-533(2019).

[31] J. Xu, Y. Huang, M.-M. Cheng. Noisy-As-Clean: Learning self-supervised denoising from corrupted image. IEEE Trans. Image Process., 29, 9316-9329(2020).

[32] T. Huang, S. Li, X. Jia. Neighbor2Neighbor: self-supervised denoising from single noisy images. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 14781-14790(2021).

[33] Y. Quan, M. Chen, T. Pang. Self2Self with dropout: learning self-supervised denoising from single image. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1890-1898(2020).

[34] T. Huang, S. Li, X. Jia. Neighbor2Neighbor: a self-supervised framework for deep image denoising. IEEE Trans. Image Process., 31, 4023-4038(2022).

[35] J. Lequyer, R. Philip, A. Sharma. A fast blind zero-shot denoiser. Nat. Mach. Intell., 4, 953-963(2022).

[36] M. Eom, S. Han, P. Park. Statistically unbiased prediction enables accurate denoising of voltage imaging data. Nat. Methods, 20, 1581-1592(2023).

[37] M. Zontak, M. Irani. Internal statistics of a single natural image. Proceedings of the Conference on IEEE Computer Vision and Pattern Recognition, 977-984(2011).

[38] https://github.com/utixx/SelfMirror/blob/main/Supplementary%20Information/Supplementary_Information_of_SelfMirror.pdf.

[39] A. Song, J. L. Gauthier, J. W. Pillow. Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods. J. Neurosci. Methods, 358, 109173(2021).

[40] D. Ulyanov, A. Vedaldi, V. Lempitsky. Deep image prior. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 9446-9454(2018).

[41] L. Qu, S. Zhao, Y. Huang. Self-inspired learning for denoising live-cell super-resolution microscopy. Nat. Methods, 21, 1895-1908(2024).

[42] M. Zhou, H. Yu, J. Huang. Deep Fourier up-sampling. Proceedings of the 36th International Conference on Neural Information Processing Systems, 22995-23008(2022).

[43] J. Schindelin, I. Arganda-Carreras, E. Frise. Fiji: an open-source platform for biological-image analysis. Nat. Methods, 9, 676-682(2012).

[44] L. Streich, J. C. Boffi, L. Wang. High-resolution structural and functional deep brain imaging using adaptive optics three-photon microscopy. Nat. Methods, 18, 1253-1258(2021).

[45] C. H. McCollough, A. C. Bartley, R. E. Carter. Low-dose CT for the detection and classification of metastatic liver lesions: results of the 2016 low dose CT grand challenge. Med. Phys., 44, e339-e352(2017).

[46] M. Berning, K. M. Boergens, M. Helmstaedter. Segem: efficient image analysis for high-resolution connectomics. Neuron, 87, 1193-1206(2015).

[47] https://github.com/utixx/SelfMirror.