College - Author 1
College of Science and Mathematics
Department - Author 1
Biological Sciences Department
College - Author 2
College of Science and Mathematics
Department - Author 2
Computer Science Department
College - Author 4
College of Engineering
Department - Author 4
Computer Science Department
Advisor
Jonathan Ventura, College of Engineering, Computer Science & Software Engineering
Funding Source
National Institutes of Health (NIH)
Date
10-2024
Abstract/Summary
In order to avoid damaging live cells, optical microscope imaging must be conducted under low-excitation light intensity and/or short exposure times, resulting in low signal-to-noise ratios (SNR). Deep learning methods offer an effective solution for removing microscope noise, utilizing algorithms that are able to reconstruct finer features in low SNR images. This research explores the denoising capability of several deep learning methods based on PSNR and SSIM. Tested methods include traditional approaches (BMED), supervised learning (CARE and Restormer), and unsupervised methods (Noise2Fast, N2V, SSD-Unsupervised, and SASSID). The Restormer model, which employs an encoder-decoder transformer architecture and progressive learning, stood out from other methods, demonstrating strong PSNR and SSIM performance.
October 1, 2024.
Included in
Artificial Intelligence and Robotics Commons, Bioinformatics Commons, Cells Commons, Other Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Structural Biology Commons
URL: https://digitalcommons.calpoly.edu/ceng_surp/83