Superslicing frame restoration for anisotropic sstem

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 an approach 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 benchmark, where SuperSlicing preserves topological structures significantly better than other algorithms.