In　order　to　remove　the　seams　and　ensure　information　details　are　not　missed　in　the　image　fusion,　the　multi-band　blending　image　fusion　method　for　multi-fundus　image　was　proposed　in　this　paper　based　on　hierarchical　optimization　fusion.　The　mask　image　is　achieved　by　multi-threshold　segmentation　and　Euclidean　distance　transform.　And　the　improved　weighted　coefficients　are　designed　based　on　the　distance　value　in　the　overlap　region　and　the　layers　of　Gaussian　image　sum　of　Laplacian　energy　for　the　mask　image.　The　combined　objective　evaluation　was　proposed　that　it　is　based　on　information　entropy,　spatial　frequency　and　definition.　The　registration　error　and　overlap　rate　are　used　to　realize　hierarchical　optimization　multi-band　image　fusion　method　for　grouping　fusion.　And　the　image　alone　is　put　into　next　level　directly.　75　group　image　sequences　are　applied　involving　4　898　set　of　images　(normal　fundus　image　2　952　pairs　and　disease　fundus　image　1　946　pairs)　for　testing　and　validation.　The　results　show　that　the　proposed　method　is　effective　that　removes　the　seam　and　obtains　the　RMSE　value　of　about　(0.　1　±0.　05)　pixels　in　overlap　region.　The　proposed　method　can　make　equilibrium　between　objective　evaluation　and　subjective　visual　effect.