Image-adaptive deconvolution for three-dimensional deep biological imaging

Deconvolution algorithms are widely used in conventional fluorescence microscopy, but they remain difficult to apply to deep imaging systems such as confocal and two-photon microscopy, due to the practical difficulty of measuring the system's point spread function (PSF), especially in biological experiments. Since a separate PSF measurement performed under the design optical conditions of the microscope cannot reproduce the true experimental conditions prevailing in situ, the most natural approach to solve the problem is to extract the PSF from the images themselves. We investigate here the approach of cropping an approximate PSF directly from the images, by exploiting the presence of small structures within the samples under study. This approach turns out to be practical in many cases, allowing significantly better restorations than with a design PSF obtained by imaging fluorescent beads in gel. We demonstrate the advantages of this approach with a number of deconvolution experiments performed both on artificially blurred and noisy test images, and on real confocal images taken within an in vitro preparation of the mouse hearing organ.