Adaptive Prior and Long-Range Dependency-Based Learners for Image Inpainting

Image inpainting attempts to fill in missing areas of corrupted images. Previous works used diverse prior information as constraints to recover high-quality images. Nevertheless, these priors rely on heuristic information and highly empirical selection. Moreover, CNN-based methods ignore the global long-range dependencies between spatial positions in images. This paper presents adaptive prior and long-range dependency-based learners (APLRL) for image inpainting. It mainly constructs an adaptive prior extractor (AdaPE) and an adaptive graph convolution (AdaGConv) operator. Specifically, AdaPE devises a learnable network by integrating partial convolution into residual learning. This enables it to mitigate the pollution of prior information caused by mask influence, effectively learn and extract any unknown explicit and implicit priors in a data-driven manner, and assist in image inpainting. Besides, an AdaGConv operator adaptively learns potential sparse graph structures in images by a learnable threshold strategy, and fuses graph convolution operators to acquire long-distance information on image spatial locations. This improves comprehension of the image’s overall structure and contributes to the network filling in the missing areas more effectively. Experiments reveal the superiority of APLRL over different baselines. Notably, AdaPE provides a readily transferable plug-and-play module. The source code is available at https://github.com/QijinXu/APLRL.