Computational microscopy explained

Computational microscopy is a subfield of computational imaging, which combines algorithmic reconstruction with sensing to capture microscopic images of objects.^{[1] [2]} The algorithms used in computational microscopy often combine the information of several images captured using various illuminations or measurements to form an aggregated 2D or 3D image using iterative techniques or machine learning.^{[3] [4]} Notable forms of computational microscopy include super-resolution fluorescence microscopy, quantitative phase imaging, and Fourier ptychography.^{[5] [6] [7] [8] [9] [10] [11] [12] [13] [14]} Computational microscopy is at the intersection of computer science and optics.^{[15] [16] [17]}

Notes and References

1. Ikoma, Hayato. "Computational microscopy for sample analysis." PhD diss., Massachusetts Institute of Technology, 2014.
2. de Haan, Kevin, Yair Rivenson, Yichen Wu, and Aydogan Ozcan. "Deep-learning-based image reconstruction and enhancement in optical microscopy." Proceedings of the IEEE 108, no. 1 (2019): 30-50.
3. Waller, Laura, and Lei Tian. "Computational imaging: Machine learning for 3D microscopy." Nature 523.7561 (2015): 416-417.
4. Yeh, Li-Hao, Shwetadwip Chowdhury, Nicole A. Repina, and Laura Waller. "Speckle-structured illumination for 3D phase and fluorescence computational microscopy." Biomedical optics express 10, no. 7 (2019): 3635-3653.
5. McLeod, Euan, and Aydogan Ozcan. "Unconventional methods of imaging: computational microscopy and compact implementations." Reports on Progress in Physics 79, no. 7 (2016): 076001.
6. Zheng, Guoan, Roarke Horstmeyer and Changhuei Yang. "Wide-field, high-resolution Fourier ptychographic microscopy." Nature Photonics 7, pp. 739-45 (2013).
7. Ou, Xiaoze, Guoan Zheng and Changhuei Yang. "Embedded pupil function recovery for Fourier ptychographic microscopy." Optics Express 22, 4960-72 (2014)
8. Horstmeyer, Roarke, Jaebum Chung, Xiaoze Ou, Guoan Zheng and Changhuei Yang. "Diffraction tomography with Fourier ptychography." Optica 3, pp. 827-835 (2016)
9. Zheng, Guoan, Cheng Shen, Shaowei Jiang, Pengming Song, Changhuei Yang. "Concept, implementations and applications of Fourier Ptychography." Nature Reviews Physics 3, 207 (2021)
10. Ou, Xiaoze, Roarke Horstmeyer, Guoan Zheng and Changhuei Yang."High numerical aperture Fourier ptychography: principle, implementation and characterization." Optics Express 23, pp. 3472-91 (2015).
11. Horstmeyer, Roarke. "Computational microscopy: Turning megapixels into gigapixels." 2016.
12. Pham, Minh. "New Algorithms in Computational Microscopy." PhD diss., UCLA, 2020.
13. Chen, Claire Lifan, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang, Kayvan Reza Niazi, and Bahram Jalali. "Deep learning in label-free cell classification." Scientific Reports 6 (2016): 21471.
14. Yuan, Shuai, and Chrysanthe Preza. "Point-spread function engineering to reduce the impact of spherical aberration on 3D computational fluorescence microscopy imaging." Optics Express 19, no. 23 (2011): 23298-23314.
15. Hollmann, Joseph L., Andrew K. Dunn, and Charles A. DiMarzio. "Computational microscopy in embryo imaging." Optics Letters 29, no. 19 (2004): 2267-2269.
16. Eggeling, Christian, and Alf Honigmann. "Closing the gap: the approach of optical and computational microscopy to uncover biomembrane organization." Biochimica et Biophysica Acta (BBA) - Biomembranes 1858, no. 10 (2016): 2558-2568.
17. Greenbaum, Alon, Yibo Zhang, Alborz Feizi, Ping-Luen Chung, Wei Luo, Shivani R. Kandukuri, and Aydogan Ozcan. "Wide-field computational imaging of pathology slides using lens-free on-chip microscopy." Science translational medicine 6, no. 267 (2014): 267ra175-267ra175.