Image inpainting is a challenging task in image processing and widely applied in areas such as image restoration and 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 due to the discontinuity of the local pixels. To solve this problem, we propose a novel image inpainting method based on wavelet transform attention model. The wavelet transform decomposes features into high frequency and low frequency subbands for extracting and transmitting deep information, and the attention mechanism enhances the ability of wavelet transform to capture significant multi-frequency information. Extensive experimental results on multiple datasets(CelebA, CelebA-HQ and Paris StreetView) 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.