Sonar　technology　plays　an　important　role　in　the　development　of　marine　resources　and　military　strategy.　Due　to　the　bad　underwater　acoustic　channel,　the　sonar　image　collected　by　sonar　technology　equipment　is　affected　by　various　kinds　of　distortions　easily.　To　obtain　high-quality　sonar　image,　we　devise　a　novel　dual-path　deep　neural　network　(DPDNN)　to　measure　the　quality　of　sonar　image.　In　these　two　paths,　we　use　the　batch　normalization　layer　to　reduce　the　training　time　and　take　the　skip　operation　to　speed　up　the　feature　extraction.　Based　on　the　above　two　operations,　we　extract　the　micro-scopic　and　macro-scopic　structure　of　sonar　image,　respectively.　Finally,　the　global　average　pooling　layer　and　the　fully　connection　layer　are　used　to　connect　the　above　two　paths.　Experiments　show　that　our　DPDNN　has　a　significant　improvement　in　prediction　performance　and　efficiency,　respectively.　©　2020　IEEE.