Image Inpainting with External-internal Learning and Monochromic Bottleneck
This work addresses artifact issues in image inpainting for computer vision applications, representing an incremental improvement over existing deep learning methods.
The paper tackled the problem of artifacts like blunt structures and abrupt colors in image inpainting by proposing an external-internal learning scheme with a monochromic bottleneck, resulting in more structure-preserved and visually compelling results as demonstrated in experiments.
Although recent inpainting approaches have demonstrated significant improvements with deep neural networks, they still suffer from artifacts such as blunt structures and abrupt colors when filling in the missing regions. To address these issues, we propose an external-internal inpainting scheme with a monochromic bottleneck that helps image inpainting models remove these artifacts. In the external learning stage, we reconstruct missing structures and details in the monochromic space to reduce the learning dimension. In the internal learning stage, we propose a novel internal color propagation method with progressive learning strategies for consistent color restoration. Extensive experiments demonstrate that our proposed scheme helps image inpainting models produce more structure-preserved and visually compelling results.