Name
Abstract | ||
|---|---|---|
The 3D point-spread function (PSF) plays a fundamental role in wide-field fluorescence microscopy. An accurate PSF estimation can significantly improve the performance of deconvolution algorithms. In this work, we propose a calibration-free method to obtain the PSF directly from the image obtained. Specifically, we first parametrize the spherically aberrated PSF as a linear combination of few basis functions. The coefficients of these basis functions are then obtained iteratively by minimizing a novel criterion, which is derived from the mixed Poisson-Gaussian noise statistics. Experiments demonstrate that the proposed approach results in highly accurate PSF estimations. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018) | point-spread function, wide-field microscopy, parametric PSF estimation, 3D deconvolution microscopy |
Field | DocType | ISSN |
Noise statistics,Linear combination,Pattern recognition,Computer science,Deconvolution,Basis function,Artificial intelligence,Microscopy,Point spread function | Conference | 1945-7928 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||