The　developments　of　generative　adversarial　networks　(GANs)　make　it　possible　to　fill　missing　regions　in　broken　images　with　convincing　details.　However,　many　existing　approaches　fail　to　keep　the　inpainted　content　and　structures　consistent　with　their　surroundings.　In　this　paper,　we　propose　a　GAN-based　inpainting　model　which　can　restore　the　semantic　damaged　images　visually　reasonable　and　coherent.　In　our　model,　the　generative　network　has　an　autoencoder　frame　and　the　discriminator　network　is　a　CNN　classifier.　Different　from　the　classic　autoencoder,　we　design　a　novel　bottleneck　layer　in　the　middle　of　the　autoencoder　which　is　comprised　of　four　dense-net　blocks　and　each　block　contains　vanilla　convolution　layers　and　dilated　convolution　layers.　The　kernels　of　dilated　convolution　are　spread　out　and　result　in　an　effective　enlargement　of　the　receptive　field.　Thus　the　model　can　capture　more　widely　semantic　information　to　ensure　the　consistency　of　inpainted　images.　Furthermore,　the　multiplex　of　different　level＇s　features　in　each　dense-net　block　can　help　the　model　understand　the　whole　image　better　to　produce　a　convincing　image.　We　evaluate　our　model　over　the　public　datasets　CelebA　and　Stanford　Cars　with　random　position　masks　of　different　ratios.　The　effectiveness　of　our　model　is　verified　by　qualitative　and　quantitative　experiments.