  Recent developments in fluorescence microscopy allow capturing
high-resolution 3D images over time for living model organisms. To be able to
image even large specimens, techniques like multi-view light-sheet imaging
record different orientations at each time point that can then be fused into a
single high-quality volume. Based on measured point spread functions (PSF),
deconvolution and content fusion are able to largely revert the inevitable
degradation occurring during the imaging process. Classical multi-view
deconvolution and fusion methods mainly use iterative procedures and
content-based averaging. Lately, Convolutional Neural Networks (CNNs) have been
deployed to approach 3D single-view deconvolution microscopy, but the
multi-view case waits to be studied. We investigated the efficacy of CNN-based
multi-view deconvolution and fusion with two synthetic data sets that mimic
developing embryos and involve either two or four complementary 3D views.
Compared with classical state-of-the-art methods, the proposed semi- and
self-supervised models achieve competitive and superior deconvolution and
fusion quality in the two-view and quad-view cases, respectively.
