Quantum　image　processing　(QIP)　means　the　quantum-based　methods　to　speed　up　image　processing　algorithms.　Many　quantum　image　processing　schemes　claim　that　their　efficiency　is　theoretically　higher　than　their　corresponding　classical　schemes.　However,　most　of　them　do　not　consider　the　problem　of　measurement.　As　we　all　know,　measurement　will　lead　to　collapse.　That　is　to　say,　executing　the　algorithm　once,　users　can　only　measure　the　final　state　one　time.　Therefore,　if　users　want　to　regain　the　results　(the　processed　images),　they　must　execute　the　algorithms　many　times　and　then　measure　the　final　state　many　times　to　get　all　the　pixels＇　values.　If　the　measurement　process　is　taken　into　account,　whether　or　not　the　algorithms　are　really　efficient　needs　to　be　reconsidered.　In　this　paper,　we　try　to　solve　the　problem　of　measurement　and　give　a　quantum　image　matching　algorithm.　Unlike　most　of　the　QIP　algorithms,　our　scheme　interests　only　one　pixel　(the　target　pixel)　instead　of　the　whole　image.　It　modifies　the　probability　of　pixels　based　on　Grover＇s　algorithm　to　make　the　target　pixel　to　be　measured　with　higher　probability,　and　the　measurement　step　is　executed　only　once.　An　example　is　given　to　explain　the　algorithm　more　vividly.　Complexity　analysis　indicates　that　the　quantum　scheme＇s　complexity　is　O(2(n))　in　contradistinction　to　the　classical　scheme＇s　complexity　O(2(2n+2m)),　where　m　and　n　are　integers　related　to　the　size　of　images.