Image　inpainting　is　a　challenging　task　in　image　processing　and　widely　applied　in　many　areas　such　as　photo　editing.　Traditional　patch-based　methods　are　not　effective　to　deal　with　complex　or　non-repetitive　structures.　Recently,　deep　learning-based　approaches　have　shown　promising　results　for　image　inpainting.　However,　they　usually　generate　contents　with　artificial　boundaries,　distorted　structures　or　blurry　textures.　To　handle　this　problem,　we　propose　a　novel　image　inpainting　method　based　on　wavelet　transform　attention　model　(WTAM).　The　wavelet　transform　decomposes　features　into　multi-frequency　subbands　for　extracting　and　transmitting　deep　information,　and　the　attention　mechanism　enhances　the　ability　of　wavelet　transform　to　capture　significant　detailed　information　in　each　level＇s　subband　images.　Extensive　experimental　results　on　multiple　datasets　(Paris　StreetView,　CelebA　and　CelebAMask-HQ)　demonstrate　that　our　method　can　not　only　synthesize　sharp　image　structures　but　also　generate　fine-detailed　textures　in　missing　regions,　significantly　outperforming　the　state-of-the-art　methods.