Low-dose　computed　tomography　(LDCT)　images　are　polluted　by　mottle　noise　and　streak　artifacts.　To　improve　LDCT　images　quality,　this　paper　proposes　a　novel　total　variation　(NTV)　model.　A　weighted　coefficient　of　the　regularization　term　of　NTV　model　is　constructed　by　standard　deviation,　gray-level　probability　and　gradient　magnitude　to　smooth　LDCT　images　adaptively,　since　the　standard　deviation　and　the　gray-level　probability　of　detail　region　are　higher　than　that　of　the　noisy　background,　and　the　gradient　magnitude　of　edges　is　higher　than　that　of　the　noisy　background.　Besides,　to　preserve　details　and　edges　effectively,　the　fidelity　term　of　the　proposed　NTV　model　is　constructed　by　the　block-matching　3d　filter　because　it　performs　well　in　details　and　edges　preservation.　The　experiments　are　performed　on　the　computer　simulated　phantom　and　the　actual　phantom.　Compared　with　several　other　competitive　methods,　both　subjective　visual　effect　and　objective　evaluation　criteria　show　that　the　proposed　NTV　model　can　improve　LDCT　images　quality　more　effectively　such　as　noise　and　artifacts　suppression,　details,　and　edges　preservation.