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Recently, flow-based methods have achieved promising success in video frameinterpolation. However, electron microscopic (EM) images suffer from unstableimage quality, low PSNR, and disorderly deformation. Existing flow-basedinterpolation methods cannot precisely compute optical flow for EM images sinceonly predicting each position's unique offset. To overcome these problems, wepropose a novel interpolation framework for EM images that progressivelysynthesizes interpolated features in a coarse-to-fine manner. First, we extractmissing intermediate features by the proposed temporal spatial-adaptive (TSA)interpolation module. The TSA interpolation module aggregates temporal contextsand then adaptively samples the spatial-related features with the proposedresidual spatial adaptive block. Second, we introduce a stacked deformablerefinement block (SDRB) further enhance the reconstruction quality, which isaware of the matching positions and relevant features from input frames withthe feedback mechanism. Experimental results demonstrate the superiorperformance of our approach compared to previous works, both quantitatively andqualitatively.

Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images