Estimation of Optical Aberrations in 3D Microscopic Bioimages
This work addresses image quality issues for microscopy users in biological research, representing an incremental advancement by adapting an existing method to new data types.
The researchers tackled the problem of optical aberrations in 3D microscopic bioimages by extending PhaseNet to work on biological samples and adding Richardson-Lucy deconvolution, resulting in improved image quality for real raw microscopic images with unknown residual PSF.
The quality of microscopy images often suffers from optical aberrations. These aberrations and their associated point spread functions have to be quantitatively estimated to restore aberrated images. The recent state-of-the-art method PhaseNet, based on a convolutional neural network, can quantify aberrations accurately but is limited to images of point light sources, e.g. fluorescent beads. In this research, we describe an extension of PhaseNet enabling its use on 3D images of biological samples. To this end, our method incorporates object-specific information into the simulated images used for training the network. Further, we add a Python-based restoration of images via Richardson-Lucy deconvolution. We demonstrate that the deconvolution with the predicted PSF can not only remove the simulated aberrations but also improve the quality of the real raw microscopic images with unknown residual PSF. We provide code for fast and convenient prediction and correction of aberrations.