Sonar　imaging　system　plays　a　crucial　role　in　ocean　exploration　since　it　can　overcome　the　limitations　of　light　conditions.　However,　the　challenge　of　low　resolution　remains　in　sonar　images　(SIs)　due　to　sonar　imaging　characteristics　and　varying　compression　for　low-bandwidth　transmission.　Most　existing　image　super-resolution　(SR)　methods　treat　both　the　structure　and　texture　in　the　same　way,　thus　failing　to　simultaneously　capture　the　rich　global-local　information.　Nevertheless,　both　structure　and　texture　are　essential　for　the　visual　quality　and　applications　of　SIs.　In　this　study,　we　propose　a　structure-texture　dual-preserving　network　(STDPNet)　tailored　to　capture　both　local　texture　details　and　global　structure　in　a　parallel　manner　for　SISR.　To　further　explore　the　internal　correlation　between　structure　and　texture　features,　a　feature　interaction　strategy　is　introduced.　Moreover,　conventional　loss　functions　for　SR　often　yield　smooth　results.　We　propose　a　hybrid　loss　function　with　spectral　and　local　gradient-aware　components　to　preserve　frequency　content　and　enhance　texture　detail.　Experimental　results　validate　the　superior　performance　of　the　proposed　STDPNet.