Sonar　sensors　are　vital　in　the　marine　industry　for　detecting　underwater　targets　in　challenging　conditions.　The　imaging　distance　and　image　resolution　are　negatively　correlated　due　to　the　propagation　characteristics　of　sound　waves　in　water.　Although　Super-Resolution　(SR)　techniques　alleviate　this　limitation,　they　introduce　complex　distortions　that　may　not　fit　the　desired　utility　of　reconstructed　sonar　images.　Quantifying　image　quality　is　essential,　yet　existing　Image　Quality　Assessment　(IQA)　algorithms　fail　to　simultaneously　consider　reconstruction　distortions　and　the　sonar　image　task　background.　Furthermore,　the　scarcity　of　sonar　images　poses　challenges　for　deep-learning-based　algorithms.　To　address　these　issues,　we　propose　a　brain-inspired　model　for　Super-Resolution　reconstructed　Sonar　Image　Quality　Assessment　(SRSIQA)　based　on　transfer　learning.　On　the　one　hand,　we　adopt　an　effective　feature　extractor　to　extract　multi-level　features　that　align　with　the　human　brain　recognition　process.　On　the　other　hand,　we　develop　an　SEA-Block　to　implement　feature　weight　adjustment　and　multi-level　feature　scale　matching,　given　that　some　transferred　features　do　not　fit　the　IQA　task　well.　Experimental　results　demonstrate　the　superiority　of　the　proposed　method.　©　2023　Elsevier　B.V.