To solve of the defects of the existing image inpainting algorithms， such as the lack of contextual information and effective perceptual field， leading to poor performance when recovering large random damages and being restricted to low-resolution images， a parallel fast Fourier convolution generation inpainting algorithm based on residual transformer is proposed. Firstly， a transformer-based improved residual network module is proposed to extract the texture features from the image to be inpainted. Subsequently， a parallel fast Fourier convolution module is designed to enhance highly effective sensory field and capture the structural information from the corrupt areas. Finally， the gated dual-feature fusion module is developed to exchange and combine the structural and texture components of the images to fuse the contextual features and improve the fine-grained nature of the generated textures. Qualitative and quantitative experiments are conducted on two public datasets， and the experimental results show that the proposed algorithm can effectively restore random irregular large broken regions with complex structures and fine textures， generate high-fidelity images with reasonable structures， fine textures and rich semantics， and can be used for target removal of high-resolution images.