The　broad　applicability　of　super-resolution　microscopy　has　been　widely　demonstrated　in　various　areas　and　disciplines.　The　optimization　and　improvement　of　algorithms　used　in　super-resolution　microscopy　are　of　great　importance　for　achieving　optimal　quality　of　super-resolution　imaging.　In　this　review,　we　comprehensively　discuss　the　computational　methods　in　different　types　of　super-resolution　microscopy,　including　deconvolution　microscopy,　polarization-based　super-resolution　microscopy,　structured　illumination　microscopy,　image　scanning　microscopy,　super-resolution　optical　fluctuation　imaging　microscopy,　single-molecule　localization　microscopy,　Bayesian　super-resolution　microscopy,　stimulated　emission　depletion　microscopy,　and　translation　microscopy.　The　development　of　novel　computational　methods　would　greatly　benefit　super-resolution　microscopy　and　lead　to　better　resolution,　improved　accuracy,　and　faster　image　processing.