  We present a new reconstruction of the Event Horizon Telescope (EHT) image of
the M87 black hole from the 2017 data set. We use PRIMO, a novel
dictionary-learning based algorithm that uses high-fidelity simulations of
accreting black holes as a training set. By learning the correlations between
the different regions of the space of interferometric data, this approach
allows us to recover high-fidelity images even in the presence of sparse
coverage and reach the nominal resolution of the EHT array. The black hole
image comprises a thin bright ring with a diameter of $41.5\pm0.6\,\mu$as and a
fractional width that is at least a factor of two smaller than previously
reported. This improvement has important implications for measuring the mass of
the central black hole in M87 based on the EHT images.
