Deep Learning Deconvolution

Reto Fiolka Lab; Project mentor: Bo-Jui Chang

Have you been impressed by the license plate recognition in the CSI (Crime Scene Investigation) TV series? Have you ever wondered how they do it? Similar tasks also exist in biomedical imaging, in which scientists try to enhance image quality or/and improve image resolution, to resolve sub-cellular structures. Here, deconvolution is the key and the very often used technique to tackle this task.

Conventional deconvolution techniques such as Lucy-Richardson deconvolution can theoretically improve the resolution by a factor of 1.41(). However, it involves iterations, which is time consuming, especially when dealing with large 3D image volumes. More critically, it requires critical user input that is tedious to define and can significantly bias the content of the processed data.

In this project we will use deep learning to perform image deconvolution (1). We anticipate that deep learning deconvolution will achieve same or similar results as conventional deconvolution, but at a much faster speed (10-100x) and without need for user input. In the future, deep learning can also be used to further enhance super-resolution microscopy.

1. Guo, M. et al. Rapid image deconvolution and multiview fusion for optical microscopy. Nat. Biotechnol. 1–10 (2020). doi:10.1038/s41587-020-0560-x

For more information about this project email Bo-Jui Chang.