In　the　existing　image　style　transferring　methods,　the　primary　structure　of　the　target　image　is　often　deformed　while　a　distinctive　style　is　transferred.　Therefore,　a　loss　function　for　style　transferring　based　on　DCNN　is　designed.　In　addition　to　the　original　items,　two　more　regularization　terms　are　introduced　in　the　function.　To　maintain　the　primary　structure　of　the　target　image,　the　edge　information　extracted　by　the　LoG　operator　is　used　as　the　feature　for　the　primary　structure.　The　first　regularization　term　is　constituted　by　feature　difference　between　the　resultant　image　and　the　target　image.　The　second　regularization　term　is　composed　of　features　obtained　by　Gabor　filter　to　enhance　the　description　of　directional　style　features　since　artistic　style　is　closely　related　to　directional　characteristics　such　as　strokes,　texture　orientation　and　color　flow　while　depth　features　are　more　focused　on　depicting　global　information.　This　item　avoids　the　weakening　effect　of　transferred　style　due　to　the　maintenance　of　the　primary　structure.　The　experimental　results　show　that　the　proposed　method　maintains　better　primary　structure　of　the　target　image　while　successfully　transferring　a　distinctive　style.　©　2018,　Science　Press.　All　right　reserved.