[{"_id":"2945","publication_status":"published","date_created":"2023-05-20T15:51:00Z","title":"Impact of different loss functions on denoising of microscopic images","status":"public","conference":{"location":"Padua, Italy","name":"2022 International Joint Conference on Neural Networks (IJCNN)"},"language":[{"iso":"eng"}],"doi":"10.1109/IJCNN55064.2022.9892936","citation":{"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","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.","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","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.","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.","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"},"page":"1-10","publication_identifier":{"eisbn":["978-1-7281-8671-9"]},"author":[{"last_name":"Shah","full_name":"Shah, Zafran Hussain","first_name":"Zafran Hussain","id":"239296"},{"first_name":"Marcel","full_name":"Muller, Marcel","last_name":"Muller"},{"last_name":"Hammer","full_name":"Hammer, Barbara","first_name":"Barbara"},{"first_name":"Thomas","full_name":"Huser, Thomas","last_name":"Huser"},{"id":"224375","orcid":"0000-0003-3300-2048","last_name":"Schenck","first_name":"Wolfram","full_name":"Schenck, Wolfram"}],"quality_controlled":"1","type":"conference","year":"2022","date_updated":"2023-06-19T15:01:12Z","publisher":"IEEE","publication":"2022 International Joint Conference on Neural Networks (IJCNN)","user_id":"245590"},{"language":[{"iso":"eng"}],"doi":"10.1364/PRJ.416437","citation":{"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} }","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.","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","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.","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.","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","short":"Z.H. Shah, M. Müller, T.-C. Wang, P.M. Scheidig, A. Schneider, M. Schüttpelz, T. Huser, W. Schenck, Photonics Research 9 (2021)."},"article_number":"B168","publication_identifier":{"eissn":["2327-9125"]},"author":[{"id":"239296","last_name":"Shah","first_name":"Zafran Hussain","full_name":"Shah, Zafran Hussain"},{"last_name":"Müller","full_name":"Müller, Marcel","first_name":"Marcel"},{"first_name":"Tung-Cheng","full_name":"Wang, Tung-Cheng","last_name":"Wang"},{"last_name":"Scheidig","first_name":"Philip Maurice","full_name":"Scheidig, Philip Maurice"},{"full_name":"Schneider, Axel","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0002-6632-3473/work/94914657","first_name":"Axel","last_name":"Schneider","orcid":"0000-0002-6632-3473","id":"213480"},{"first_name":"Mark","full_name":"Schüttpelz, Mark","last_name":"Schüttpelz"},{"last_name":"Huser","full_name":"Huser, Thomas","first_name":"Thomas"},{"first_name":"Wolfram","full_name":"Schenck, Wolfram","orcid_put_code_url":"https://api.orcid.org/v2.0/0000-0003-3300-2048/work/94914472","last_name":"Schenck","id":"224375","orcid":"0000-0003-3300-2048"}],"publication_status":"published","_id":"1201","title":"Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images","date_created":"2021-06-03T19:35:46Z","project":[{"name":"CareTech OWL - Zentrum für Gesundheit, Soziales und Technologie","_id":"72dfeb62-b436-11ed-9513-f39505d26204"},{"_id":"edf53067-b368-11ed-bde2-9f34a4102af5","name":"TransCareTech - Transformation in Care & Technology"},{"_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b","name":"Institut für Systemdynamik und Mechatronik"}],"status":"public","date_updated":"2023-05-30T15:19:56Z","issue":"5","volume":9,"publisher":"The Optical Society","publication":"Photonics Research","user_id":"245590","type":"journal_article","oa":"1","intvolume":" 9","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1364/PRJ.416437"}],"year":"2021"},{"author":[{"id":"239296","last_name":"Shah","full_name":"Shah, Zafran Hussain","first_name":"Zafran Hussain"},{"first_name":"Marcel","full_name":"Müller, Marcel","last_name":"Müller"},{"last_name":"Wang","first_name":"Tung-Cheng","full_name":"Wang, Tung-Cheng"},{"last_name":"Scheidig","full_name":"Scheidig, Philip Maurice","first_name":"Philip Maurice"},{"last_name":"Schneider","first_name":"Axel","full_name":"Schneider, Axel","id":"213480","orcid":"0000-0002-6632-3473"},{"last_name":"Schüttpelz","first_name":"Mark","full_name":"Schüttpelz, Mark"},{"last_name":"Huser","first_name":"Thomas","full_name":"Huser, Thomas"},{"id":"224375","orcid":"0000-0003-3300-2048","last_name":"Schenck","first_name":"Wolfram","full_name":"Schenck, Wolfram"}],"language":[{"iso":"eng"}],"citation":{"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","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.","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","ieee":"Z. H. Shah et al., Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Cold Spring Harbor Laboratory, 2020.","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.","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"},"doi":"https://doi.org/10.1101/2020.10.27.352633","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"},{"name":"Institut für Systemdynamik und Mechatronik","_id":"beb248c8-cd75-11ed-b77c-e432b4711f7b"}],"status":"public","abstract":[{"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 ","lang":"eng"}],"publication_status":"published","_id":"2778","date_created":"2023-04-18T21:54:24Z","title":"Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images","publisher":"Cold Spring Harbor Laboratory","user_id":"245590","date_updated":"2023-05-30T15:14:55Z","main_file_link":[{"url":"https://doi.org/10.1101/2020.10.27.352633","open_access":"1"}],"year":"2020","oa":"1","type":"working_paper"}]