C.V. Raman, K.S. Krishnan, A new type of secondary radiation. Nature121, 501-502 (1928)

R.W. Boyd, Nonlinear optics (Academic press, Cambridge, 2020)

J.-X. Cheng, X.S. Xie, Coherent Raman scattering microscopy (CRC Press, Boca Raton, 2016)

J.-X. Cheng, W. Min, Y. Ozeki, D. Polli, Stimulated Raman scattering microscopy: techniques and applications (Elsevier, Amsterdam, 2021)

R. Terhune, P. Maker, C. Savage, Measurements of nonlinear light scattering. Phys. Rev. Lett. 14, 681 (1965)

E. Woodbury, W. Ng, Ruby laser operation in near IR. Proc. Inst. Radio Eng. 50, 2367-3000 (1962)

M.D. Duncan, J. Reintjes, T.J. Manuccia, Scanning coherent anti-stokes raman microscope. Opt. Lett. 7, 350-352 (1982)

A. Zumbusch, G.R. Holtom, X.S. Xie, Three-dimensional vibrational imaging by coherent anti-Stokes Raman scattering. Phys. Rev. Lett. 82,4142-4145 (1999)

E.M. Vartiainen, Phase retrieval approach for coherent anti-Stokes-Raman scattering spectrum analysis. J. Opt. Soc. Am. B9, 1209-1214(1992)

E.M. Vartiainen, H.A. Rinia, M. Muller, M. Bonn, Direct extraction of Raman line-shapes from congested CARS spectra. Opt. Express14, 3622-3630 (2006)

Y.X. Liu, Y.J. Lee, M.T. Cicerone, Broadband CARS spectral phase retrieval using a time-domain Kramers-Kronig transform. Opt. Lett. 34, 1363-1365 (2009)

M.T. Cicerone, K.A. Aamer, Y.J. Lee, E. Vartiainen, Maximum entropy and time-domain Kramers-Kronig phase retrieval approaches are functionally equivalent for CARS microspectroscopy. J. Raman Spectrosc. 43, 637-643 (2012)

E. Ploetz, S. Laimgruber, S. Berner, W. Zinth, P. Gilch, Femtosecond stimulated Raman microscopy. Appl. Phys. B87, 389-393 (2007)

C.W. Freudiger et al., Label-free biomedical imaging with high sensitivity by stimulated raman scattering microscopy. Science322, 1857-1861 (2008)

D.L. Zhang, M.N. Slipchenko, D.E. Leaird, A.M. Weiner, J.X. Cheng, Spectrally modulated stimulated Raman scattering imaging with an angle-to-wavelength pulse shaper. Opt. Express21, 13864-13874 (2013)

J.-X. Cheng, L.D. Book, X.S. Xie, Polarization coherent anti-Stokes Raman scattering microscopy. Opt. Lett. 26, 1341-1343 (2001)

B.G. Saar et al., Video-rate molecular imaging in vivo with stimulated raman scattering. Science330, 1368-1370 (2010)

H.F. Wang, Y. Fu, P. Zickmund, R.Y. Shi, J.X. Cheng, Coherent anti-stokes Raman scattering imaging of axonal myelin in live spinal tissues. Bio-phys. J. 89, 581-591 (2005)

C.L. Evans et al., Chemical imaging of tissue in vivo with video-rate coherent anti-Stokes Raman scattering microscopy. Proc. Natl. Acad. Sci.USA102, 16807-16812 (2005)

F.K. Lu et al., Label-free DNA imaging in vivo with stimulated Raman scattering microscopy. Proc. Natl. Acad. Sci. USA112, 11624-11629(2015)

D.L. Zhang et al., Quantitative vibrational imaging by hyperspectral stimulated raman scattering microscopy and multivariate curve resolution analysis. Anal. Chem. 85, 98-106 (2013)

Y. Ozeki et al., High-speed molecular spectral imaging of tissue with stimulated Raman scattering. Nat. Photonics6, 844-850 (2012)

J.X. Cheng, A. Volkmer, L.D. Book, X.S. Xie, Multiplex coherent anti-stokes Raman scattering microspectroscopy and study of lipid vesicles. J. Phys.Chem. B106, 8493-8498 (2002)

K. Seto, Y. Okuda, E. Tokunaga, T. Kobayashi, Development of a multiplex stimulated Raman microscope for spectral imaging through multichannel lock-in detection. Rev. Sci. Instrum. 84, 083705 (2013)

C.S. Liao et al., Microsecond scale vibrational spectroscopic imaging by multiplex stimulated Raman scattering microscopy. Light Sci. Appl. 4,e265 (2015)

T. Hellerer, A.M.K. Enejder, A. Zumbusch, Spectral focusing: high spectral resolution spectroscopy with broad-bandwidth laser pulses. Appl. Phys.Lett. 85, 25-27 (2004)

D. Fu, G. Holtom, C. Freudiger, X. Zhang, X.S. Xie, Hyperspectral imaging with stimulated raman scattering by chirped femtosecond lasers. J.Phys. Chem. B117, 4634-4640 (2013)

E.R. Andresen, P. Berto, H. Rigneault, Stimulated Raman scattering microscopy by spectral focusing and fiber-generated soliton as Stokes pulse. Opt. Lett. 36, 2387-2389 (2011)

C.H. Camp et al., High-speed coherent Raman fingerprint imaging of biological tissues. Nat. Photonics8,627-634 (2014)

W.W. Chen et al., Spectroscopic coherent Raman imaging of Caeno-rhabditis elegans reveals lipid particle diversity. Nat. Chem. Biol. 16,1087-1095 (2020)

B. Figueroa et al., Broadband hyperspectral stimulated Raman scattering microscopy with a parabolic fiber amplifier source. Biomed. Opt.Express9, 6116-6131 (2018)

Y. Suzuki et al., Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering. Proc. Natl. Acad. Sci.USA116, 15842-15848 (2019)

Y. Wakisaka et al., Probing the metabolic heterogeneity of live Euglena gracilis with stimulated Raman scattering microscopy. Nat. Microbiol. 1,16124 (2016)

R.Y. He et al., Stimulated Raman scattering microscopy and spectroscopy with a rapid scanning optical delay line. Opt. Lett. 42,659-662(2017)

C.S. Liao et al., Stimulated Raman spectroscopic imaging by microsecond delay-line tuning. Optica3, 1377-1380 (2016)

H.N. Lin et al., Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning. Nat. Commun.12, 1-2 (2021)

M. Tamamitsu et al., Ultrafast broadband Fourier-transform CARS spectroscopy at 50,000 spectra/s enabled by a scanning Fourier-domain delay line. Vib. Spectrosc. 91, 163-169 (2017)

K. Hiramatsu et al., High-throughput label-free molecular fingerprinting flow cytometry. Sci. Adv. (2019). https://doi.org/10.1126/sciadv.aau0241https://doi.org/10.1126/sciadv.aau0241

C. Zhang et al., Stimulated Raman scattering flow cytometry for label-free single-particle analysis. Optica4, 103-109 (2017)

N. Nitta et al., Raman image-activated cell sorting. Nat. Commun. 11,1-6 (2020)

H.Z. Li et al., Imaging chemical kinetics of radical polymerization with an ultrafast coherent Raman microscope. Adv. Sci. 7, 1903644 (2020)

Y. Ozeki, F. Dake, S. Kajiyama, K. Fukui, K. Itoh, Analysis and experimental assessment of the sensitivity of stimulated Raman scattering microscopy. Opt. Express17, 3651-3658 (2009)

W.L. Hong et al., In situ detection of a single bacterium in complex environment by hyperspectral CARS imaging. ChemistrySelect1, 513-517(2016)

S. Yampolsky et al., Seeing a single molecule vibrate through time-resolved coherent anti-Stokes Raman scattering. Nat. Photonics8,650-656 (2014)

C. Zong et al., Plasmon-enhanced stimulated Raman scattering microscopy with single-molecule detection sensitivity. Nat. Commun. 10, 1(2019)

C. Zong et al., Wide-field surface-enhanced coherent anti-Stokes Raman scattering microscopy. ACS Photonics9,1042-1049 (2022)

H.K. Wickramasinghe, M. Chaigneau, R. Yasukuni, G. Picardi, R. Ossiko-vski, Billion-fold increase in tip-enhanced Raman signal. ACS Nano8,3421-3426 (2014)

I. Rajapaksa, H.K. Wickramasinghe, Raman spectroscopy and microscopy based on mechanical force detection. Appl. Phys. Lett. 99, 161103 (2011)

L. Wei, W. Min, Electronic preresonance stimulated Raman scattering microscopy. J. Phys. Chem. Lett. 9, 4294-4301 (2018)

H.Q. Xiong et al., Stimulated Raman excited fluorescence spectroscopy and imaging. Nat. Photonics13, 412 (2019)

H.Q. Xiong et al., Super-resolution vibrational microscopy by stimulated Raman excited fluorescence. Light Sci. Appl. 10, 1 (2021)

F. Fereidouni, A.N. Bader, H.C. Gerritsen, Spectral phasor analysis allows rapid and reliable unmixing of fluorescence microscopy spectral images. Opt. Express20, 12729-12741 (2012)

A.H.A. Clayton, Q.S. Hanley, P.J. Verveer, Graphical representation and multicomponent analysis of single-frequency fluorescence lifetime imaging microscopy data. J. Microsc. 213, 1-5 (2004)

F.E. Robles, J.W. Wilson, M.C. Fischer, W.S. Warren, Phasor analysis for nonlinear pump-probe microscopy. Opt. Express20, 17082-17092(2012)

D. Fu, X.S. Xie, Reliable cell segmentation based on spectral phasor analysis of hyperspectral stimulated Raman scattering imaging data.Anal. Chem.86, 4115-4119 (2014)

J. Zhang, J. Zhao, H.N. Lin, Y.Y. Tan, J.X. Cheng, High-speed chemical imaging by dense-net learning of femtosecond stimulated raman scattering. J. Phys. Chem. Lett. 11, 8573-8578 (2020)

J. Jaumot, R. Gargallo, A. de Juan, R. Tauler, A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB. Chemometr. Intell. Lab. 76,101-110 (2005)

P. Wang et al., Imaging lipid metabolism in live caenorhabditis elegans using fingerprint vibrations. Angew. Chem. Int. Ed. 53,11787-11792(2014)

H.N. Lin, C.S. Liao, P. Wang, N. Kong, J.X. Cheng, Spectroscopic stimulated Raman scattering imaging of highly dynamic specimens through matrix completion. Light Sci. Appl. 7, 17179 (2018)

F. Masia, A. Glen, P. Stephens, P. Borri, W. Langbein, Quantitative chemical imaging and unsupervised analysis using hyperspectral coherent anti-Stokes Raman scattering microscopy. Anal Chem85, 10820-10828(2013)

J. Kim, H. Park, Fast nonnegative matrix factorization: an active-set-like method and comparisons. SIAM J. Sci. Comput. 33, 3261-3281 (2011)

F. Masia, A. Karuna, P. Borri, W. Langbein, Hyperspectral image analysis for CARS, SRS, and Raman data. J. Raman Spectrosc. 46, 727-734 (2015)

K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process16, 2080-2095 (2007)

A. Buades, B. Coll, J.M. Morel, A non-local algorithm for image denoising.Proc. CVPR IEEE2, 60-65 (2005)

G.A. Zheng, R. Horstmeyer, C.H. Yang, Wide-field, high-resolution Fourier ptychographic microscopy. Nat. Photonics7, 739-745 (2013)

M.G.L. Gustafsson, Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy. J. Microsc. 198, 82-87(2000)

E.J. Candes, M.B. Wakin, An introduction to compressive sampling. IEEE Signal Proc. Mag. 25, 21-30 (2008)

P. Berto, C. Scotte, F. Galland, H. Rigneault, H.B. de Aguiar, Programmable single-pixel-based broadband stimulated Raman scattering. Opt. Lett. 42, 1696-1699 (2017)

S. Takizawa, K. Hiramatsu, K. Goda, Compressed time-domain coherent Raman spectroscopy with real-time random sampling. Vib. Spectrosc.107, 103042 (2020)

E.J. Candes, Y. Plan, Matrix completion with noise. Proc. IEEE 98, 925-936(2010)

C.W. Freudiger et al., Highly specific label-free molecular imaging with spectrally tailored excitation-stimulated Raman scattering (STE-SRS)microscopy. Nat Photonics5, 103-109 (2011)

K. Bae, W. Zheng, Z.W. Huang, Spatial light-modulated stimulated Raman scattering (SLM-SRS) microscopy for rapid multiplexed vibra-tional imaging. Theranostics10, 312-322 (2020)

F. Masia, P. Borri, W. Langbein, Sparse sampling for fast hyperspectral coherent anti-Stokes Raman scattering imaging. Opt. Express22,4021-4028 (2014)

I.J. Pence, B.A. Kuzma, M. Brinkmann, T. Hellwig, C.L. Evans, Multi-win-dow sparse spectral sampling stimulated Raman scattering microscopy.Biomed. Opt. Express12, 6095-6114 (2021)

X. Yu, J.S. Hong, C.G. Liu, M.K. Kim, Review of digital holographic microscopy for three-dimensional profiling and tracking. Opt. Eng. 53, 12306(2014)

M. Levoy, R. Ng, A. Adams, M. Footer, M. Horowitz, Light field microscopy. ACM Trans. Graphic25, 924-934 (2006)

Y.J. Sung et al., Optical diffraction tomography for high resolution live cell imaging. Opt Express17, 266-277 (2009)

K.B. Shi, H.F. Li, Q. Xu, D. Psaltis, Z.W. Liu, Coherent anti-Stokes Raman holography for chemically selective single-shot nonscanning 3D imaging. Phys. Rev. Lett. (2010). https://doi.org/10.1103/PhysRevLett.104.093902https://doi.org/10.1103/PhysRevLett.104.093902

A. Cocking, N. Mehta, K.B. Shi, Z.W. Liu, Compressive coherent anti-Stokes Raman scattering holography. Opt. Express23, 24991-24996(2015)

X.L. Chen et al., Volumetric chemical imaging by stimulated Raman projection microscopy and tomography. Nat. Commun. (2017). https://doi.org/10.1038/ncomms15117https://doi.org/10.1038/ncomms15117

P. Lin et al., Tilt-angle stimulated Raman projection tomography. Opt.Express30, 37112-37123 (2022)

D. Fu et al., Quantitative chemical imaging with multiplex stimulated Raman scattering microscopy. J. Am. Chem. Soc. 134, 3623-3626 (2012)

C.S. Liao et al., Spectrometer-free vibrational imaging by retrieving stimulated Raman signal from highly scattered photons. Sci. Adv.(2015). https://doi.org/10.1126/sciadv.1500738https://doi.org/10.1126/sciadv.1500738

S. Heuke et al., Spatial frequency modulated imaging in coherent anti-Stokes Raman microscopy. Optica7, 417-424 (2020)

L. Gong, S.L. Lin, Z.W. Huang, Stimulated Raman scattering tomography enables label-free volumetric deep tissue imaging. Laser Photonics Rev.15,2100069 (2021)

G. Barbastathis, A. Ozcan, G. Situ, On the use of deep learning for computational imaging. Optica6,921-943 (2019)

M. Weigert et al., Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods15, 1090 (2018)

H.D. Wang et al., Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods16, 103 (2019)

C. Ounkomol, S. Seshamani, M.M. Maleckar, F. Collman, G.R. Johnson, Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat. Methods15, 917 (2018)

E.M. Christiansen et al., In silico labeling: predicting fluorescent labels in unlabeled images. Cell173, 792 (2018)

J.C. Caicedo et al., Data-analysis strategies for image-based cell profiling. Nat. Methods14, 849-863 (2017)

J.B. Lugagne, H.N. Lin, M.J. Dunlop, DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning. Plos Comput. Biol. (2020). https://doi.org/10.1371/journal.pcbi.1007673https://doi.org/10.1371/journal.pcbi.1007673

W. Ouyang, A. Aristov, M. Lelek, X. Hao, C. Zimmer, Deep learning massively accelerates super-resolution localization microscopy. Nat.Biotechnol. 36, 460 (2018)

E. Nehme et al., DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning. Nat. Methods17, 734 (2020)

C.R. Vogel, M.E. Oman, Iterative methods for total variation denoising. SIAM J. Sci. Comput. 17, 227-238 (1996)

C.S. Liao, J.H. Choi, D.L. Zhang, S.H. Chan, J.X. Cheng, Denoising stimu-lated raman spectroscopic images by total variation minimization. J.Phys Chem. C. 119,19397-19403 (2015)

B. Manifold, E. Thomas, A.T. Francis, A.H. Hill, D. Fu, Denoising of stimu-lated Raman scattering microscopy images via deep learning. Biomed.Opt. Express10, 3860-3874 (2019)

N. Yamato, H. Niioka, J. Miyake, M. Hashimoto, Improvement of nerve imaging speed with coherent anti-Stokes Raman scattering rigid endoscope using deep-learning noise reduction. Sci. Rep. (2020). https://doi.org/10.1038/s41598-020-72241-xhttps://doi.org/10.1038/s41598-020-72241-x

Qiu Z.F., Yao, T. & Mei, T. Learning spatio-temporal representation with pseudo-3d residual networks. In: Proceedings of the IEEE International Conference on Computer Vision 2017, pp. 5533-41

P. Abdolghader et al., Unsupervised hyperspectral stimulated Raman microscopy image enhancement: denoising and segmentation via one-shot deep learning. Opt Express29, 34205-34219 (2021)

F. Vernuccio et al., Fingerprint multiplex CARS at high speed based on supercontinuum generation in bulk media and deep learning spectral denoising. Opt Express30, 30135-30148 (2022)

M. Lee et al., Recent advances in the use of stimulated Raman scattering in histopathology. Analyst146, 789-802 (2021)

G. Litjens et al., A survey on deep learning in medical image analysis. Med Image Anal42, 60-88 (2017)

D.A. Orringer et al., Rapid intraoperative histology of unprocessed surgical specimens via fibre-laser-based stimulated Raman scattering microscopy. Nat. Biomed. Eng. (2017). https://doi.org/10.1038/s41551-016-0027https://doi.org/10.1038/s41551-016-0027

T.C. Hollon et al., Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat Med26, 52-58 (2020)

L.L. Zhang et al., Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy. Theranostics9, 2541-2554 (2019)

A. Feizpour, T. Marstrand, L. Bastholm, S. Eirefelt, C.L. Evans, Label-free quantification of pharmacokinetics in skin with stimulated raman scattering microscopy and deep learning. J. Invest. Dermatol. 141, 395-403(2021)

P. Manescu et al., Accurate and interpretable classification of microspec-troscopy pixels using artificial neural networks. Med. Image Anal. 37, 37-45 (2017)

K. Aljakouch et al., Fast and noninvasive diagnosis of cervical cancer by coherent anti-stokes raman scattering. Anal. Chem91, 13900-13906(2019)

S. Weng, X.Y. Xu, J.S. Li, S.T.C. Wong, Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer. J. Biomed. Opt. (2017). https://doi.org/10.1117/1.JBO.22.10.106017https://doi.org/10.1117/1.JBO.22.10.106017

R. Houhou et al., Deep learning as phase retrieval tool for CARS spectra.Opt. Express28, 21002-21024 (2020)

C.M. Valensise et al., Removing non-resonant background from CARS spectra via deep learning. APL Photonics5, 061305 (2020)

Z.W. Wang et al., VECTOR: Very deep convolutional autoencoders for non-resonant background removal in broadband coherent anti-Stokes Raman scattering. J. Raman Spectrosc. 53, 1081-1093 (2022)

A. Bresci et al., Removal of cross-phase modulation artifacts in ultrafast pump-probe dynamics by deep learning. APL Photonics6, 076104(2021)

Y. Rivenson et al., Deep learning microscopy. Optica4, 1437-1443(2017)

B. Manifold, S.Q. Men, R.Q. Hu, D. Fu, A versatile deep learning architecture for classification and label-free prediction of hyperspectral images. Nat. Mach. Intell. 3, 306 (2021)

Huang G., Liu, Z., van der Maaten, L. & Weinberger, K.Q.Densely connected convolutional networks. 30th Ieee Conference on Computer Vision and Pattern Recognition (CVPR 2017), (2017), pp. 2261-9

Z.J. Liu et al., Instant diagnosis of gastroscopic biopsy via deep-learned single-shot femtosecond stimulated Raman histology. Nat. Commun. (2022). https://doi.org/10.1038/s41467-022-31339-8https://doi.org/10.1038/s41467-022-31339-8