Denoising-based Image Compression for Connectomics
Minnen, D.; Januszewski, M.; Shapson-Coe, A.; Schalek, R. L.; Balle, J.; Lichtman, J. W.; Jain, V.
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Connectomic reconstruction of neural circuits relies on nanometer resolution microscopy which produces on the order of a petabyte of imagery for each cubic millimeter of brain tissue. The cost of storing such data is a significant barrier to broadening the use of connectomic approaches and scaling to even larger volumes. We present an image compression approach that uses machine learning-based denoising and standard image codecs to compress raw electron microscopy imagery of neuropil up to 17-fold with negligible loss of 3d reconstruction and synaptic detection accuracy.
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