Auto-correlation for multi-view deconvolved reconstruction in light sheet microscopy

Tomographic inspection of fluorescent labels distributed within a specimen is an important aspect in biology. Light sheet fluorescent microscopy (LSFM) offers a powerful and simple tool to selectively slice the sample and let us directly obtain a tomographic view of the specimen. However, due to non-isotropic resolution of the technique along the axial scanning, one may want to combine different views of the object and add deconvolution to the process in order to achieve higher resolution. Typically, multi-view Bayesian methods based on Richardson-Lucy deconvolution are used for this task once the datasets are exactly registered against each other. In this work, instead, we begin to investigate how to avoid the alignment procedure and use a direct algorithm to form a multi-view tomographic reconstruction. To do this, we developed a new framework based on auto-correlation analysis that let us achieve deconvolved reconstructions starting from blurred auto-correlations. Since the latter are insensitive to shifts, we can combine the auto-correlations coming from multi-view acquisitions without taking care of the registration procedure.