Deep generative approaches have obtained great success in image inpainting recently. However, most generative inpainting networks suffer from either over-smooth results or aliasing artifacts. The former lacks high-frequency details, while the latter lacks semantic structure. To address this issue, we propose an effective Frequency-Spatial Complementary Network (FSCN) by exploiting rich semantic information in both spatial and frequency domains. Specifically, we introduce an extra Frequency Branch and Frequency Loss on the spatial-based network to impose direct supervision on the frequency information, and propose a Frequency-Spatial Cross-Attention Block (FSCAB) to fuse multi-domain features and combine the corresponding characteristics. With our FSCAB, the inpainting network is capable of capturing frequency information and preserving visual consistency simultaneously. Extensive quantitative and qualitative experiments demonstrate that our inpainting network can effectively achieve superior results, outperforming previous state-of-the-art approaches with significantly fewer parameters and less computation cost. The code will be released soon.

该研究提出了一种有效的频空间补偿网络(Frequency-Spatial Complementary Network)，通过利用空间和频率域中丰富的语义信息建立了额外的频率支路和频率损失，并提出了一种频率-空间跨域注意力块(Frequency-Spatial Cross-Attention Block)来融合多域特征和结合对应的特征。通过测试和量化实验，证明了该方法在保留高频细节、语义结构和视觉一致性方面表现优异。

空间和频率提示对高保真图像修复的贡献