Image adaptive point-spread function estimation and deconvolution for in vivo confocal microscopy.

Visualizing deep inside the tissue of a thick biological sample often poses severe constraints on image conditions. Standard restoration techniques (denoising and deconvolution) can then be very useful, allowing one to increase the signal-to-noise ratio and the resolution of the images. In this paper, we consider the problem of obtaining a good determination of the point-spread function (PSF) of a confocal microscope, a prerequisite for applying deconvolution to three-dimensional image stacks acquired with this system. Because of scattering and optical distortion induced by the sample, the PSF has to be acquired anew for each experiment. To tackle this problem, we used a screening approach to estimate the PSF adaptively and automatically from the images. Small PSF-like structures were detected in the images, and a theoretical PSF model reshaped to match the geometric characteristics of these structures. We used numerical experiments to quantify the sensitivity of our detection method, and we demonstrated its usefulness by deconvolving images of the hearing organ acquired in vitro and in vivo.