Blind deconvolution in autocorrelation inversion for multiview light‐sheet microscopy. Issue 6 (24th February 2022)

• Record Type: Journal Article
• Authors: Corbetta, Elena; Candeo, Alessia; Bassi, Andrea; Ancora, Daniele
• Is Part Of: Microscopy research and technique. Volume 85:Issue 6(2022)
• Page Start: 2282
• Page End: 2291
• Publication Date: 2022-02-24
• Subjects: Electron microscopy -- Technique -- Periodicals; Microscopy -- Periodicals; Microscopy -- Technique -- Periodicals; 502.825
• DOI: 10.1002/jemt.24085
• Languages: English
• ISSNs: 1059-910X

Abstract

Abstract: Combining the information coming from multiview acquisitions is a problem of great interest in light‐sheet microscopy. Aligning the views and increasing the resolution of their fusion can be challenging, especially if the setup is not fully calibrated. Here, we tackle these issues by proposing a new reconstruction method based on autocorrelation inversion that avoids alignment procedures. On top of this, we add a blind deconvolution step to improve the resolution of the final reconstruction. Our method permits us to achieve inherently aligned, highly resolved reconstructions while, at the same time, estimating the unknown point‐spread function of the system. Research Highlights: We tackle the problem of multiview light‐sheet deconvolution with a blind approach of autocorrelation inversion Our method recovers the object and PSF, requires no alignment and calibration, and enhances the reconstruction of the specimen. Abstract : In this publication, we propose a new method for image reconstruction in light‐sheet microscopy based on multi‐view acquisitions. While reconstructing, our algorithm also estimates the point‐spread function of the microscope, increasing the resolution of the reconstructed image simultaneously. This protocol does not need initial alignment of the dataset and starts with a blind estimation of the PSF, rendering it a flexible solution for automatic high‐resolution reconstructions. The image shows a synthetic multi‐view sample, blindly deconvolved with the proposed inversion scheme: blurred and noisy object (top‐left triangle) and final reconstruction (bottom‐right triangle).