--- _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' ...