Stitched　fluorescence　microscope　images　inevitably　exist　in　various　types　of　stripes　or　artifacts　caused　by　uncertain　factors　such　as　optical　devices　or　specimens,　which　severely　affects　the　image　quality　and　downstream　quantitative　analysis.　Here,　we　present　a　deep　learning-based　Stripe　Self-Correction　method,　so-called　SSCOR.　Specifically,　we　propose　a　proximity　sampling　scheme　and　adversarial　reciprocal　self-training　paradigm　that　enable　SSCOR　to　utilize　stripe-free　patches　sampled　from　the　stitched　microscope　image　itself　to　correct　their　adjacent　stripe　patches.　Comparing　to　off-the-shelf　approaches,　SSCOR　can　not　only　adaptively　correct　non-uniform,　oblique,　and　grid　stripes,　but　also　remove　scanning,　bubble,　and　out-of-focus　artifacts,　achieving　the　state-of-the-art　performance　across　different　imaging　conditions　and　modalities.　Moreover,　SSCOR　does　not　require　any　physical　parameter　estimation,　patch-wise　manual　annotation,　or　raw　stitched　information　in　the　correction　process.　This　provides　an　intelligent　prior-free　image　restoration　solution　for　microscopists　or　even　microscope　companies,　thus　ensuring　more　precise　biomedical　applications　for　researchers.　Image　stitching　in　fluorescence　microscopy　can　be　a　hindrance　to　image　quality　and　to　downstream　quantitative　analyses.　Here,　the　authors　propose　a　deep　learning-based　stripe　self-correction　method　that　corrects　diverse　stripes　and　artifacts　for　stitched　microscopic　images.