  Recently, flow-based methods have achieved promising success in video frame
interpolation. However, electron microscopic (EM) images suffer from unstable
image quality, low PSNR, and disorderly deformation. Existing flow-based
interpolation methods cannot precisely compute optical flow for EM images since
only predicting each position's unique offset. To overcome these problems, we
propose a novel interpolation framework for EM images that progressively
synthesizes interpolated features in a coarse-to-fine manner. First, we extract
missing intermediate features by the proposed temporal spatial-adaptive (TSA)
interpolation module. The TSA interpolation module aggregates temporal contexts
and then adaptively samples the spatial-related features with the proposed
residual spatial adaptive block. Second, we introduce a stacked deformable
refinement block (SDRB) further enhance the reconstruction quality, which is
aware of the matching positions and relevant features from input frames with
the feedback mechanism. Experimental results demonstrate the superior
performance of our approach compared to previous works, both quantitatively and
qualitatively.
