Sonar　sensors　are　widely　used　for　underwater　tasks　because　of　their　advantages　of　working　in　turbid　environment　and　having　a　long　working　range.　However,　there　is　a　trade-off　between　working　range　and　image　resolution　in　practical　applications.　For　improving　the　quality　of　the　acquired　acoustic　images,　image　super-resolution　(SR)　has　recently　drawn　considerable　attention.　Due　to　the　inconsistent　characteristics　between　natural　images　and　sonar　images,　the　existing　image　quality　assessment　(IQA)　methods　for　natural　scene　images　do　not　well　provide　constructive　suggestions　for　super-resolution　of　sonar　images.　To　solve　this　problem,　we　first　established　a　super-resolution　sonar　image　(SRSI)　database,　where　the　database　was　built　around　its　usefulness　of　image　for　marine　tasks.　Then,　a　no-reference　IQA　specifically　designed　for　SRSIs　based　on　a　brain-inspired　target　recognition　mechanism　was　proposed,　which　is　accomplished　in　a　hierarchically　organized　ventral　visual　pathway.　Experimental　results　show　that　our　database　has　certain　reliability　and　that　the　proposed　metric　outperforms　the　state-of-the-art　IQA　methods.