---
_id: '2945'
author:
- first_name: Zafran Hussain
full_name: Shah, Zafran Hussain
id: '239296'
last_name: Shah
- first_name: Marcel
full_name: Muller, Marcel
last_name: Muller
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Thomas
full_name: Huser, Thomas
last_name: Huser
- first_name: Wolfram
full_name: Schenck, Wolfram
id: '224375'
last_name: Schenck
orcid: 0000-0003-3300-2048
citation:
alphadin: 'Shah, Zafran Hussain ;
Muller, Marcel ; Hammer,
Barbara ; Huser, Thomas ;
Schenck, Wolfram: Impact of different
loss functions on denoising of microscopic images. In: 2022 International Joint
Conference on Neural Networks (IJCNN) : IEEE, 2022, S. 1–10'
ama: 'Shah ZH, Muller M, Hammer B, Huser T, Schenck W. Impact of different loss
functions on denoising of microscopic images. In: 2022 International Joint
Conference on Neural Networks (IJCNN). IEEE; 2022:1-10. doi:10.1109/IJCNN55064.2022.9892936'
apa: 'Shah, Z. H., Muller, M., Hammer, B., Huser, T., & Schenck, W. (2022).
Impact of different loss functions on denoising of microscopic images. In 2022
International Joint Conference on Neural Networks (IJCNN) (pp. 1–10). Padua,
Italy: IEEE. https://doi.org/10.1109/IJCNN55064.2022.9892936'
bibtex: '@inproceedings{Shah_Muller_Hammer_Huser_Schenck_2022, title={Impact of
different loss functions on denoising of microscopic images}, DOI={10.1109/IJCNN55064.2022.9892936},
booktitle={2022 International Joint Conference on Neural Networks (IJCNN)}, publisher={IEEE},
author={Shah, Zafran Hussain and Muller, Marcel and Hammer, Barbara and Huser,
Thomas and Schenck, Wolfram}, year={2022}, pages={1–10} }'
chicago: Shah, Zafran Hussain, Marcel Muller, Barbara Hammer, Thomas Huser, and
Wolfram Schenck. “Impact of Different Loss Functions on Denoising of Microscopic
Images.” In 2022 International Joint Conference on Neural Networks (IJCNN),
1–10. IEEE, 2022. https://doi.org/10.1109/IJCNN55064.2022.9892936.
ieee: Z. H. Shah, M. Muller, B. Hammer, T. Huser, and W. Schenck, “Impact of different
loss functions on denoising of microscopic images,” in 2022 International Joint
Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 1–10.
mla: Shah, Zafran Hussain, et al. “Impact of Different Loss Functions on Denoising
of Microscopic Images.” 2022 International Joint Conference on Neural Networks
(IJCNN), IEEE, 2022, pp. 1–10, doi:10.1109/IJCNN55064.2022.9892936.
short: 'Z.H. Shah, M. Muller, B. Hammer, T. Huser, W. Schenck, in: 2022 International
Joint Conference on Neural Networks (IJCNN), IEEE, 2022, pp. 1–10.'
conference:
location: Padua, Italy
name: 2022 International Joint Conference on Neural Networks (IJCNN)
date_created: 2023-05-20T15:51:00Z
date_updated: 2023-06-19T15:01:12Z
doi: 10.1109/IJCNN55064.2022.9892936
language:
- iso: eng
page: 1-10
publication: 2022 International Joint Conference on Neural Networks (IJCNN)
publication_identifier:
eisbn:
- 978-1-7281-8671-9
publication_status: published
publisher: IEEE
quality_controlled: '1'
status: public
title: Impact of different loss functions on denoising of microscopic images
type: conference
user_id: '245590'
year: '2022'
...
---
_id: '1201'
article_number: B168
author:
- first_name: Zafran Hussain
full_name: Shah, Zafran Hussain
id: '239296'
last_name: Shah
- first_name: Marcel
full_name: Müller, Marcel
last_name: Müller
- first_name: Tung-Cheng
full_name: Wang, Tung-Cheng
last_name: Wang
- first_name: Philip Maurice
full_name: Scheidig, Philip Maurice
last_name: Scheidig
- first_name: Axel
full_name: Schneider, Axel
id: '213480'
last_name: Schneider
orcid: 0000-0002-6632-3473
orcid_put_code_url: https://api.orcid.org/v2.0/0000-0002-6632-3473/work/94914657
- first_name: Mark
full_name: Schüttpelz, Mark
last_name: Schüttpelz
- first_name: Thomas
full_name: Huser, Thomas
last_name: Huser
- first_name: Wolfram
full_name: Schenck, Wolfram
id: '224375'
last_name: Schenck
orcid: 0000-0003-3300-2048
orcid_put_code_url: https://api.orcid.org/v2.0/0000-0003-3300-2048/work/94914472
citation:
alphadin: 'Shah, Zafran Hussain ;
Müller, Marcel ; Wang,
Tung-Cheng ; Scheidig, Philip Maurice
; Schneider, Axel ; Schüttpelz,
Mark ; Huser, Thomas ; Schenck, Wolfram: Deep-learning based
denoising and reconstruction of super-resolution structured illumination microscopy
images. In: Photonics Research Bd. 9, The Optical Society (2021), Nr. 5'
ama: 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). doi:10.1364/PRJ.416437
apa: Shah, Z. H., Müller, M., Wang, T.-C., Scheidig, P. M., Schneider, A., Schüttpelz,
M., … Schenck, W. (2021). Deep-learning based denoising and reconstruction of
super-resolution structured illumination microscopy images. Photonics Research,
9(5). https://doi.org/10.1364/PRJ.416437
bibtex: '@article{Shah_Müller_Wang_Scheidig_Schneider_Schüttpelz_Huser_Schenck_2021,
title={Deep-learning based denoising and reconstruction of super-resolution structured
illumination microscopy images}, volume={9}, DOI={10.1364/PRJ.416437},
number={5B168}, journal={Photonics Research}, publisher={The Optical Society},
author={Shah, Zafran Hussain and Müller, Marcel and Wang, Tung-Cheng and Scheidig,
Philip Maurice and Schneider, Axel and Schüttpelz, Mark and Huser, Thomas and
Schenck, Wolfram}, year={2021} }'
chicago: Shah, Zafran Hussain, Marcel Müller, Tung-Cheng Wang, Philip Maurice Scheidig,
Axel Schneider, Mark Schüttpelz, Thomas Huser, and Wolfram Schenck. “Deep-Learning
Based Denoising and Reconstruction of Super-Resolution Structured Illumination
Microscopy Images.” Photonics Research 9, no. 5 (2021). https://doi.org/10.1364/PRJ.416437.
ieee: Z. H. Shah et al., “Deep-learning based denoising and reconstruction
of super-resolution structured illumination microscopy images,” Photonics Research,
vol. 9, no. 5, 2021.
mla: Shah, Zafran Hussain, et al. “Deep-Learning Based Denoising and Reconstruction
of Super-Resolution Structured Illumination Microscopy Images.” Photonics Research,
vol. 9, no. 5, B168, The Optical Society, 2021, doi:10.1364/PRJ.416437.
short: Z.H. Shah, M. Müller, T.-C. Wang, P.M. Scheidig, A. Schneider, M. Schüttpelz,
T. Huser, W. Schenck, Photonics Research 9 (2021).
date_created: 2021-06-03T19:35:46Z
date_updated: 2023-05-30T15:19:56Z
doi: 10.1364/PRJ.416437
intvolume: ' 9'
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1364/PRJ.416437
oa: '1'
project:
- _id: 72dfeb62-b436-11ed-9513-f39505d26204
name: CareTech OWL - Zentrum für Gesundheit, Soziales und Technologie
- _id: edf53067-b368-11ed-bde2-9f34a4102af5
name: TransCareTech - Transformation in Care & Technology
- _id: beb248c8-cd75-11ed-b77c-e432b4711f7b
name: Institut für Systemdynamik und Mechatronik
publication: Photonics Research
publication_identifier:
eissn:
- 2327-9125
publication_status: published
publisher: The Optical Society
status: public
title: Deep-learning based denoising and reconstruction of super-resolution structured
illumination microscopy images
type: journal_article
user_id: '245590'
volume: 9
year: '2021'
...
---
_id: '2778'
abstract:
- lang: eng
text: " Abstract - \r\n Super-resolution structured
illumination microscopy (SR-SIM) provides an up to two-fold 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 times,
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 convolution neural network (RED-Net) was used to successfully
denoise computationally reconstructed noisy SR-SIM images. We also demonstrate
the entirely 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
over various noise levels. The combination of computational reconstruction and
subsequent denoising via RED-Net shows very robust performance during inference
after training even if the microscope settings change.\r\n "
author:
- first_name: Zafran Hussain
full_name: Shah, Zafran Hussain
id: '239296'
last_name: Shah
- first_name: Marcel
full_name: Müller, Marcel
last_name: Müller
- first_name: Tung-Cheng
full_name: Wang, Tung-Cheng
last_name: Wang
- first_name: Philip Maurice
full_name: Scheidig, Philip Maurice
last_name: Scheidig
- first_name: Axel
full_name: Schneider, Axel
id: '213480'
last_name: Schneider
orcid: 0000-0002-6632-3473
- first_name: Mark
full_name: Schüttpelz, Mark
last_name: Schüttpelz
- first_name: Thomas
full_name: Huser, Thomas
last_name: Huser
- first_name: Wolfram
full_name: Schenck, Wolfram
id: '224375'
last_name: Schenck
orcid: 0000-0003-3300-2048
citation:
alphadin: 'Shah, Zafran Hussain ;
Müller, Marcel ; Wang,
Tung-Cheng ; Scheidig, Philip Maurice
; Schneider, Axel ; Schüttpelz,
Mark ; Huser, Thomas ; Schenck, Wolfram: Deep-learning based
denoising and reconstruction of super-resolution structured illumination microscopy
images : Cold Spring Harbor Laboratory, 2020'
ama: Shah ZH, Müller M, Wang T-C, et al. Deep-Learning Based Denoising and Reconstruction
of Super-Resolution Structured Illumination Microscopy Images. Cold Spring
Harbor Laboratory; 2020. doi:https://doi.org/10.1101/2020.10.27.352633
apa: Shah, Z. H., Müller, M., Wang, T.-C., Scheidig, P. M., Schneider, A., Schüttpelz,
M., … Schenck, W. (2020). Deep-learning based denoising and reconstruction
of super-resolution structured illumination microscopy images. Cold Spring
Harbor Laboratory. https://doi.org/10.1101/2020.10.27.352633
bibtex: '@book{Shah_Müller_Wang_Scheidig_Schneider_Schüttpelz_Huser_Schenck_2020,
title={Deep-learning based denoising and reconstruction of super-resolution structured
illumination microscopy images}, DOI={https://doi.org/10.1101/2020.10.27.352633},
publisher={Cold Spring Harbor Laboratory}, author={Shah, Zafran Hussain and Müller,
Marcel and Wang, Tung-Cheng and Scheidig, Philip Maurice and Schneider, Axel and
Schüttpelz, Mark and Huser, Thomas and Schenck, Wolfram}, year={2020} }'
chicago: Shah, Zafran Hussain, Marcel Müller, Tung-Cheng Wang, Philip Maurice Scheidig,
Axel Schneider, Mark Schüttpelz, Thomas Huser, and Wolfram Schenck. Deep-Learning
Based Denoising and Reconstruction of Super-Resolution Structured Illumination
Microscopy Images. Cold Spring Harbor Laboratory, 2020. https://doi.org/10.1101/2020.10.27.352633.
ieee: Z. H. Shah et al., Deep-learning based denoising and reconstruction
of super-resolution structured illumination microscopy images. Cold Spring
Harbor Laboratory, 2020.
mla: Shah, Zafran Hussain, et al. Deep-Learning Based Denoising and Reconstruction
of Super-Resolution Structured Illumination Microscopy Images. Cold Spring
Harbor Laboratory, 2020, doi:https://doi.org/10.1101/2020.10.27.352633.
short: Z.H. Shah, M. Müller, T.-C. Wang, P.M. Scheidig, A. Schneider, M. Schüttpelz,
T. Huser, W. Schenck, Deep-Learning Based Denoising and Reconstruction of Super-Resolution
Structured Illumination Microscopy Images, Cold Spring Harbor Laboratory, 2020.
date_created: 2023-04-18T21:54:24Z
date_updated: 2023-05-30T15:14:55Z
doi: https://doi.org/10.1101/2020.10.27.352633
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2020.10.27.352633
oa: '1'
project:
- _id: 72dfeb62-b436-11ed-9513-f39505d26204
name: CareTech OWL - Zentrum für Gesundheit, Soziales und Technologie
- _id: edf53067-b368-11ed-bde2-9f34a4102af5
name: TransCareTech - Transformation in Care & Technology
- _id: beb248c8-cd75-11ed-b77c-e432b4711f7b
name: Institut für Systemdynamik und Mechatronik
publication_status: published
publisher: Cold Spring Harbor Laboratory
status: public
title: Deep-learning based denoising and reconstruction of super-resolution structured
illumination microscopy images
type: working_paper
user_id: '245590'
year: '2020'
...