  Microscopy imaging is vital in biology research and diagnosis. When imaging
at the scale of cell or molecule level, mechanical drift on the axial axis can
be difficult to correct. Although multi-scale networks have been developed for
deblurring, those cascade residual learning approaches fail to accurately
capture the end-to-end non-linearity of deconvolution, a relation between
in-focus images and their out-of-focus counterparts in microscopy. In our
model, we adopt a structure of multi-scale U-Net without cascade residual
leaning. Additionally, in contrast to the conventional coarse-to-fine model,
our model strengthens the cross-scale interaction by fusing the features from
the coarser sub-networks with the finer ones in a head-to-tail manner: the
decoder from the coarser scale is fused with the encoder of the finer ones.
Such interaction contributes to better feature learning as fusion happens
across decoder and encoder at all scales. Numerous experiments demonstrate that
our method yields better performance when compared with other existing models.
