Speaker
Description
Cross-correlation is a versatile mathematical technique for analyzing image data. It provides insights into spatial distributions, temporal dynamics, and geometric colocalization by quantifying relationships between image components. With this project, we explore a specific application of cross-correlation, namely the autocorrelation, to boost image resolution in post-processing. We demonstrate how this simple mathematical tool can be used to combine multiple images of the same object, captured under different imaging conditions, into a single, higher-resolution representation. This approach offers a promising avenue for image reconstruction, particularly in multi-view light-sheet and image scanning microscopy. Building on this foundational research, we identify areas for enhancement and propose strategies to expand the method's capabilities, aiming at generalizing the approach to include more imaging methodologies.
| Authors | Daniele Ancora*, Alessandro Zunino, Gianluca Valentini, Antonio Pifferi, Andrea Bassi, Giuseppe Vicidomini, Alvaro Crevenna |
|---|---|
| Keywords | image processing, autocorrelation inversion, phase retrieval, image reconstruction, fluorescence microscopy |
