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.　Characteristics　of　the　wavelet　coefficients　are　used　to　adaptively　select　fusion　rules.　A　weighting　method　based　on　PCA　is　applied　to　low-frequency　image　components,　and　the　regional　variance　estimation　is　applied　to　high-frequency　components　including　edges　and　details　of　the　original　image.　Experiments　reveal　that　the　methods　are　effective　for　multifocus,　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.