Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images

Shah ZH, Müller M, Wang T-C, Scheidig PM, Schneider A, Schüttpelz M, Huser T, Schenck W (2021)
Photonics Research 9(5): B168.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Shah, Zafran Hussain; Müller, MarcelUniBi; Wang, Tung-Cheng; Scheidig, Philip Maurice; Schneider, AxelUniBi; Schüttpelz, MarkUniBi ; Huser, ThomasUniBi ; Schenck, WolframUniBi
Abstract / Bemerkung
Super-resolution structured illumination microscopy (SR-SIM) provides an up to twofold enhanced spatial resolution of fluorescently labeled samples. The reconstruction of high-quality SR-SIM images critically depends on patterned illumination with high modulation contrast. Noisy raw image data (e.g., as a result of low excitation power or low exposure time), result in reconstruction artifacts. Here, we demonstrate deep-learning based SR-SIM image denoising that results in high-quality reconstructed images. A residual encoding–decoding convolutional neural network (RED-Net) was used to successfully denoise computationally reconstructed noisy SR-SIM images. We also demonstrate the end-to-end deep-learning based denoising and reconstruction of raw SIM images into high-resolution SR-SIM images. Both image reconstruction methods prove to be very robust against image reconstruction artifacts and generalize very well across various noise levels. The combination of computational image reconstruction and subsequent denoising via RED-Net shows very robust performance during inference after training even if the microscope settings change.
Erscheinungsjahr
2021
Zeitschriftentitel
Photonics Research
Band
9
Ausgabe
5
Art.-Nr.
B168
eISSN
2327-9125
Page URI
https://pub.uni-bielefeld.de/record/2954122

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Shah ZH, Müller M, Wang T-C, et al. Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Photonics Research. 2021;9(5): B168.
Shah, Z. H., Müller, M., Wang, T. - C., Scheidig, P. M., Schneider, A., Schüttpelz, M., Huser, T., et al. (2021). Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Photonics Research, 9(5), B168. https://doi.org/10.1364/PRJ.416437
Shah, Z. H., Müller, M., Wang, T. - C., Scheidig, P. M., Schneider, A., Schüttpelz, M., Huser, T., and Schenck, W. (2021). Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Photonics Research 9:B168.
Shah, Z.H., et al., 2021. Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Photonics Research, 9(5): B168.
Z.H. Shah, et al., “Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images”, Photonics Research, vol. 9, 2021, : B168.
Shah, Z.H., Müller, M., Wang, T.-C., Scheidig, P.M., Schneider, A., Schüttpelz, M., Huser, T., Schenck, W.: Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Photonics Research. 9, : B168 (2021).
Shah, Zafran Hussain, Müller, Marcel, Wang, Tung-Cheng, Scheidig, Philip Maurice, Schneider, Axel, Schüttpelz, Mark, Huser, Thomas, and Schenck, Wolfram. “Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images”. Photonics Research 9.5 (2021): B168.
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2021-08-31T15:39:43Z
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Preprint: 10.1101/2020.10.27.352633

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