The　machine-vision　based　structural　displacement　measurement　methods　are　widely　used　due　to　its　flexible　deployment　and　non-contact　measurement　characteristics.　The　accuracy　of　vision　measurement　is　directly　related　to　the　image　resolution.　In　the　field　of　computer　vision,　super-resolution　reconstruction　is　an　emerging　method　to　improve　image　resolution.　Particularly,　the　deep-learning　based　image　super-resolution　methods　have　shown　great　potential　for　improving　image　resolution　and　thus　the　machine-vision　based　measurement.　In　this　article,　we　firstly　review　the　latest　progress　of　several　deep　learning　based　super-resolution　models,　together　with　the　public　benchmark　datasets　and　the　performance　evaluation　index.　Secondly,　we　construct　a　binocular　visual　measurement　platform　to　measure　the　distances　of　the　adjacent　corners　on　a　chessboard　that　is　universally　used　as　a　target　when　measuring　the　structure　displacement　via　machine-vision　based　approaches.　And　then,　several　typical　deep　learning　based　super　resolution　algorithms　are　employed　to　improve　the　visual　measurement　performance.　Experimental　results　show　that　super-resolution　reconstruction　technology　can　improve　the　accuracy　of　distance　measurement　of　adjacent　corners.　According　to　the　experimental　results,　one　can　find　that　the　measurement　accuracy　improvement　of　the　super　resolution　algorithms　is　not　consistent　with　the　existing　quantitative　performance　evaluation　index.　Lastly,　the　current　challenges　and　future　trends　of　super　resolution　algorithms　for　visual　measurement　applications　are　pointed　out.