Many　deep　learning-based　approaches　have　been　authenticated　well　performed　for　low-dose　computed　tomography　(LDCT)　image　postprocessing.　Unfortunately,　most　of　them　highly　depend　on　well-paired　datasets,　which　are　difficult　to　acquire　in　clinical　practice.　Therefore,　we　propose　an　improved　cycle-consistent　adversarial　networks　(CycleGAN)　to　improve　the　quality　of　LDCT　images.　We　employ　a　UNet-based　network　with　attention　gates　ensembled　as　the　generator,　which　could　adaptively　stress　salient　features　which　is　useful　for　the　denoising　task.　By　doing　so,　the　proposed　network　could　enable　the　decoder　to　acquire　available　semantic　features　from　the　encoder　with　emphasis,　thereby　improving　its　performance.　Then,　perceptual　loss　found　on　the　visual　geometry　group　(VGG)　is　drawn　into　the　cycle　consistency　loss　to　elevate　the　visual　effect　of　denoised　images　to　that　of　standard-dose　computed　tomography　images　as　far　as　possible.　Moreover,　we　raise　an　ameliorative　adversarial　loss　based　on　the　least　square　loss.　In　particular,　the　Lipschitz　constraint　is　added　to　the　objective　function　of　the　discriminator,　while　total　variation　is　added　to　that　of　the　generator,　to　further　enhance　the　denoising　capability　of　the　network.　The　proposed　method　is　trained　and　tested　on　a　public　dataset　named　＇Lung-PET-CT-Dx＇　and　a　real　clinical　dataset.　Results　show　that　the　proposed　method　outperforms　the　comparative　methods　and　even　performs　comparably　results　to　that　of　an　approach　based　on　paired　datasets　in　terms　of　quantitative　scores　and　visual　sense.