Date of Award
2025
First Advisor
Kenneth Knox
Second Advisor
Amanda Landi
Abstract
Multiphoton microscopy (MPM) is a useful tool for imaging live, deep, and label free tissues. But, using point scanning in most MPM platforms causes a balance issue between time, field of view (FOV), phototoxicity, and image quality. This often causes noisy images, especially when quick, large FOV, deep, or non-invasive imaging is needed. While deep learning has the potential to denoise MPM data, there is still risk of hallucinations in these algorithms, posing significant challenges for medical and scientific applications. In this project, we introduce a dataset de- signed to facilitate deep learning-based denoising, thereby enhancing the reliability of algorithms for predictions based on deep learning. Additionally, by employ ing this dataset to fine tune a neural network, we demonstrate the preservation of fine features and superior performance compared to other denoising methods.
Recommended Citation
Liu, Yanru, "Multi Photon Microscopy Denoising" (2025). Senior Theses. 1725.
https://digitalcommons.bard.edu/sr-theses/1725
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