Infrared　(IR)　and　visible　(VI)　image　fusion　play　an　important　role　in　improving　ability　to　scene　perception　and　target　detection,　however,　due　to　different　imaging　principles,　significant　feature　differences　of　images　make　it　very　difficult　to　extract　and　integrate　feature　information　effectively,　especially　in　complex　scenes　where　the　target　feature　has　different　scales　and　contrast.　Therefore,　this　paper　proposes　an　image　fusion　method　based　on　scale-aware　edge-preserving　filter　and　weighted　least　square　optimization,　aiming　to　extract　features　at　different　scales　more　accurately.　First,　we　designed　a　hybrid　feature　decomposition　method　based　on　the　scale-aware　structure-preserving　filter　and　Gaussian　filter.　The　proposed　method　separated　source　images　into　region,　structure,　and　texture　layers,　and　thus　achieved　a　finer-scale　division　than　traditional　multiscale　decomposition　methods.　Then,　according　to　the　characteristics　of　infrared　and　visible　images　in　the　region　layer　and　texture　layer,　the　weighted　least　squares　optimization　framework　is　used　combing　with　visual　saliency　map　and　scale-aware　mechanism　respectively,　to　obtain　better　visual　expression　effect.　Experimental　results　indicated　that　the　proposed　method　could　achieve　better　subjective　and　objective　results　than　current　state-of-the-art　methods.　©　2022　ACM.