Summarize this content to 100 words: Raman, C. V. & Krishnan, K. S. A new type of secondary radiation. Nature 121, 501–502 (1928).Article
Google Scholar
Levin, I. W. & Bhargava, R. Fourier transform infrared vibrational spectroscopic imaging: integrating microscopy and molecular recognition. Annu. Rev. Phys. Chem. 56, 429–474 (2005).Article
Google Scholar
Li, X. et al. Spatial redundancy transformer for self-supervised fluorescence image denoising. Nat. Comput. Sci. 3, 1067–1080 (2023).Article
Google Scholar
Lu, Z. et al. Virtual-scanning light-field microscopy for robust snapshot high-resolution volumetric imaging. Nat. Methods 20, 735–746 (2023).Article
Google Scholar
Li, X. et al. Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising. Nat. Methods 18, 1395–1400 (2021).Article
Google Scholar
Zhang, K. et al. An end-to-end recurrent compressed sensing method to denoise, detect and demix calcium imaging data. Nat. Mach. Intell. 6, 1106–1118 (2024).
Google Scholar
Zou, Z. et al. A deep learning model for predicting selected organic molecular spectra. Nat. Comput. Sci. 3, 957–964 (2023).Article
Google Scholar
Kallepitis, C. et al. Quantitative volumetric Raman imaging of three dimensional cell cultures. Nat. Commun. 8, 14843 (2017).Article
Google Scholar
Huang, L. et al. Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning. Nat. Commun. 14, 48 (2023).Article
Google Scholar
Ilchenko, O. et al. Optics miniaturization strategy for demanding Raman spectroscopy applications. Nat. Commun. 15, 3049 (2024).Article
Google Scholar
Ferrari, A. C. & Basko, D. M. Raman spectroscopy as a versatile tool for studying the properties of graphene. Nat. Nanotechnol. 8, 235–246 (2013).Article
Google Scholar
Frosch, T. et al. Fiber-array-based Raman hyperspectral imaging for simultaneous, chemically-selective monitoring of particle size and shape of active ingredients in analgesic tablets. Molecules 24, 4381 (2019).Article
Google Scholar
Okada, M. et al. Label-free Raman observation of cytochrome c dynamics during apoptosis. Proc. Natl Acad. Sci. USA 109, 28–32 (2012).Article
Google Scholar
Wang, X. et al. Low-resolution Raman enables a low-cost, fully automated Raman microscope for microspectroscopic analysis. IEEE J. Sel. Top. Quantum Electron. 29, 1–7 (2022).
Google Scholar
Jarvis, R. M. & Goodacre, R. Discrimination of bacteria using surface-enhanced Raman spectroscopy. Anal. Chem. 76, 40–47 (2004).Article
Google Scholar
Shiota, M. et al. Gold-nanofève surface-enhanced Raman spectroscopy visualizes hypotaurine as a robust anti-oxidant consumed in cancer survival. Nat. Commun. 9, 1561 (2018).Article
Google Scholar
Son, W. K. et al. In vivo surface-enhanced Raman scattering nanosensor for the real-time monitoring of multiple stress signalling molecules in plants. Nat. Nanotechnol. 18, 205–216 (2023).Article
Google Scholar
Zumbusch, A., Holtom, G. R. & Xie, X. S. Three-dimensional vibrational imaging by coherent anti-Stokes Raman scattering. Phys. Rev. Lett. 82, 4142 (1999).Article
Google Scholar
Lin, H. & Cheng, J.-X. Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science. eLight 3, 6 (2023).Article
Google Scholar
Freudiger, C. W. et al. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science 322, 1857–1861 (2008).Article
Google Scholar
Nandakumar, P., Kovalev, A. & Volkmer, A. Vibrational imaging based on stimulated Raman scattering microscopy. New J. Phys. 11, 033026 (2009).Article
Google Scholar
Hu, F., Shi, L. & Min, W. Biological imaging of chemical bonds by stimulated Raman scattering microscopy. Nat. Methods 16, 830–842 (2019).Article
Google Scholar
Zhu, Y. et al. Stimulated Raman photothermal microscopy toward ultrasensitive chemical imaging. Sci. Adv. 9, eadi2181 (2023).Article
Google Scholar
Nair, S. et al. Algorithm-improved high-speed and non-invasive confocal Raman imaging of 2D materials. Natl Sci. Rev. 7, 620–628 (2020).Article
Google Scholar
Van Manen, H.-J., Kraan, Y. M., Roos, D. & Otto, C. Intracellular chemical imaging of heme-containing enzymes involved in innate immunity using resonance Raman microscopy. J. Phys. Chem. B 108, 18762–18771 (2004).Article
Google Scholar
Horgan, C. C. et al. High-throughput molecular imaging via deep-learning-enabled Raman spectroscopy. Anal. Chem. 93, 15850–15860 (2021).Article
Google Scholar
He, H. et al. Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy. Nat. Commun. 15, 754 (2024).Article
Google Scholar
Foi, A., Trimeche, M., Katkovnik, V. & Egiazarian, K. Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data. IEEE Trans. Image Process. 17, 1737–1754 (2008).Article
MathSciNet
Google Scholar
Zhang, Y. et al. A Poisson–Gaussian denoising dataset with real fluorescence microscopy images. In Proc. IEEE/CVFConference on Computer Vision and Pattern Recognition (CVPR) 11710–11718 (IEEE, 2019).Qiao, C. et al. Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy. Nat. Commun. 15, 4180 (2024).Article
Google Scholar
Woo, S. et al. CBAM: convolutional block attention module. In Proc. European Conference on Computer Vision (ECCV) (eds Ferrari, V. et al) 3–19 (Springer, 2018).Winter, M. E. N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data. Proc. SPIE 3753, 266–275 (1999).Heinz, D. C. et al. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 39, 529–545 (2001).Article
Google Scholar
Boyd, S. et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1–122 (2011).Article
Google Scholar
Moran, N., Schmidt, D., Zhong, Y. & Coady, P. Noisier2noise: learning to denoise from unpaired noisy data. In Proc. IEEE/CVFConference on Computer Vision and Pattern Recognition (CVPR) 12064–12072 (IEEE, 2020).Mihoubi, S., Losson, O., Mathon, B. & Macaire, L. Multispectral demosaicing using pseudo-panchromatic image. IEEE Trans. Comput. Imaging 3, 982–995 (2017).Article
MathSciNet
Google Scholar
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In International Conference on Learning Representations (ICLR) (eds Benigo, Y. & LeCunn, Y.) (2015).Ulyanov, D., Vedaldi, A. & Lempitsky, V. Deep image prior. In Proc. IEEEConference on Computer Vision and Pattern Recognition (CVPR) 9446–9454 (IEEE, 2018).Vaswani, A. Attention is all you need. Adv. Neural Inf. Process. Syst. 11, 6000–6010 (2017).
Google Scholar
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. IEEEConference on Computer Vision and Pattern Recognition (CVPR) 770–778 (IEEE, 2016).Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In Proc. IEEEConference on Computer Vision and Pattern Recognition (CVPR) 7132–7141 (IEEE, 2018).Georgiev, D. et al. RamanSPy: an open-source Python package for integrative Raman spectroscopy data analysis. Anal. Chem. 96, 8492–8500 (2024).Article
Google Scholar
Nascimento, J. M. & Dias, J. M. Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Trans. Geosci. Remote Sens. 43, 898–910 (2005).Article
Google Scholar
Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004).Article
Google Scholar
Chen, Y. et al. Self-optimized spectral distance for low-light high-throughput Raman hyperspectral imaging: source code. Code Ocean https://doi.org/10.24433/CO.4058785.v1 (2025).
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