An　algorithm　is　presented　for　exploiting　the　properties　of　the　lifting　wavelet　transform　for　multi-sensor　image　fusion.　The　method　includes　adaptive　fusion　arithmetic　based　on　principal　component　analysis　(PCA)　and　self-adaptive　regional　variance　estimation.　Different　fusion　rules　are　adopted　depending　on　the　characteristics　of　the　wavelet　Coefficients.　A　weighting　method　based　on　PCA　is　applied　to　low　frequency　image　components　referring　to　gray　value　slowly　changing　region,　and　a　self-adapting　regional　variance　estimation　method　is　applied　to　high　frequency　components　including　edge　information　and　detail　of　the　original　image.　Experiments　reveal　that　the　methods　are　effective　for　multi-focus,　visible　light,　and　infrared　image　fusion.compared　with　traditional　algorithms,　the　new　algorithm　not　only　improves　the　amount　of　preserved　information　and　clarity,　but　also　increases　the　correlation　Coefficient　between　the　fused　and　source　images.　©　2012　Binary　Information　Press.