SuperSlicing Frame Restoration for Anisotropic ssTEM and Video Data

In biological imaging the data is often represented by a sequence of anisotropic frames-- the resolution in one dimension is significantly lower than in the other dimensions. E.g. in electron microscopy it arises from the thickness of a scanned section. This leads to blurred images and raises problems in tasks like neuronal image segmentation. We present the details and additional evaluation of an approach originally introduced in Laptev et al. (2014) called SUPERSLICING to decompose the observed frame into a sequence of plausible hidden sub-frames . Based on sub-frame decomposition by SUPERSLICING we propose a novel automated method to perform neuronal structure segmentation. We test our approach on a popular connectomics benchmark, where SUPERSLICING preserves topological structures significantly better than other algorithms. We also generalize the approach for video anisotropicity that comes from the long exposure time and show that our method outperforms baseline methods on a reconstruction of low frame rate videos of natural scenes.