Sonar　technology　plays　an　important　role　in　the　development　of　marine　resources　and　military　strategy.　Due　to　the　bad　quality　of　underwater　acoustics　channels,　the　sonar　images　collected　by　sonar　technology　equipment　are　easily　affected　by　various　kinds　of　distortions.　To　obtain　high-quality　sonar　images,　the　authors　devise　a　novel　dual-path　deep　neural　network　(DPDNN)　to　measure　the　quality　of　sonar　images.　In　these　two　paths,　the　authors　use　a　batch　normalization　layer　to　reduce　the　training　time　and　use　the　skip　operation　to　speed　up　the　feature　extraction　.　Based　on　the　above　two　operations,　the　authors　extract　the　microscopic　and　macroscopic　structures　of　sonar　images,　respectively.　Finally,　a　global　average　pooling　layer　and　a　fully　connection　layer　are　used　to　connect　the　above　two　paths.　Experiments　show　that　the　authors＇　DPDNN　achieves　significant　improvements　in　prediction　performance　and　efficiency.　The　source　code　will　be　published　in　the　near　future.