The　electron　microscopy　is　one　of　the　major　means　to　observe　the　virus.　The　view　of　virus　microscope　images　is　limited　by　making　specimen　and　the　size　of　the　camera＇s　view　field.　To　solve　this　problem,　the　virus　sample　is　produced　into　multi-slice　for　information　fusion　and　image　registration　techniques　are　applied　to　obtain　large　field　and　whole　sections.　Image　registration　techniques　have　been　developed　in　the　past　decades　for　increasing　the　camera＇s　field　of　view.　Nevertheless,　these　approaches　typically　work　in　batch　mode　and　rely　on　motorized　microscopes.　Alternatively,　the　methods　are　conceived　just　to　provide　visually　pleasant　registration　for　high　overlap　ratio　image　sequence.　This　work　presents　a　method　for　virus　microscope　image　registration　acquired　with　detailed　visual　information　and　subpixel　accuracy,　even　when　overlap　ratio　of　image　sequence　is　10%　or　less.　The　method　proposed　focus　on　the　correspondence　set　and　interimage　transformation.　A　mismatch　removal　strategy　is　proposed　by　the　spatial　consistency　and　the　components　of　keypoint　to　enrich　the　correspondence　set.　And　the　translation　model　parameter　as　well　as　tonal　inhomogeneities　is　corrected　by　the　hierarchical　estimation　and　model　select.　In　the　experiments　performed,　we　tested　different　registration　approaches　and　virus　images,　confirming　that　the　translation　model　is　not　always　stationary,　despite　the　fact　that　the　images　of　the　sample　come　from　the　same　sequence.　The　mismatch　removal　strategy　makes　building　registration　of　virus　microscope　images　at　subpixel　accuracy　easier　and　optional　parameters　for　building　registration　according　to　the　hierarchical　estimation　and　model　select　strategies　make　the　proposed　method　high　precision　and　reliable　for　low　overlap　ratio　image　sequence.　©　2015　Elsevier　Ltd.