3D Microscopy Deconvolution of Very Large Images with an Adaptive Resolution Scheme
C. Ge (TU Delft - Electrical Engineering, Mathematics and Computer Science)
A-J. van der van der Veen – Mentor (TU Delft - Signal Processing Systems)
G. Joseph – Graduation committee member (TU Delft - Signal Processing Systems)
Daniel Sage – Mentor (EPFL Switzerland)
Vasiliki Stergiopoulou – Mentor (EPFL Switzerland)
Joan Rue Queralt – Mentor (EPFL Switzerland)
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Abstract
Microscopy is a crucial tool across various scientific domains. Due to light diffraction, the images acquired from optical microscopes are usually blurry and corrupted by noise. For an accurate quantitative analysis, the measured images need to be deconvolved to achieve higher resolution. Deconvolution processes are computationally expensive, due to the large data size. This leads to out-of-memory issues and extended computation time.
To address these problems, this project aims to develop a novel convolution scheme. It utilizes the special structure of the Point Spread Function, which in conventional microscopy techniques has most of its energy concentrated in the center. And implement multi-resolution signal processing methods. This approach enhances computational efficiency while retaining computational accuracy.