  Light-sheet fluorescence microscopy (LSFM), a planar illumination technique
that enables high-resolution imaging of samples, experiences defocused image
quality caused by light scattering when photons propagate through thick
tissues. To circumvent this issue, dualview imaging is helpful. It allows
various sections of the specimen to be scanned ideally by viewing the sample
from opposing orientations. Recent image fusion approaches can then be applied
to determine in-focus pixels by comparing image qualities of two views locally
and thus yield spatially inconsistent focus measures due to their limited
field-of-view. Here, we propose BigFUSE, a global context-aware image fuser
that stabilizes image fusion in LSFM by considering the global impact of photon
propagation in the specimen while determining focus-defocus based on local
image qualities. Inspired by the image formation prior in dual-view LSFM, image
fusion is considered as estimating a focus-defocus boundary using Bayes
Theorem, where (i) the effect of light scattering onto focus measures is
included within Likelihood; and (ii) the spatial consistency regarding
focus-defocus is imposed in Prior. The expectation-maximum algorithm is then
adopted to estimate the focus-defocus boundary. Competitive experimental
results show that BigFUSE is the first dual-view LSFM fuser that is able to
exclude structured artifacts when fusing information, highlighting its
abilities of automatic image fusion.
