2021/04/19 by Tengfei Wang, Wang, Tengfei, Hao Ouyang +3 · 2 citations
Computer Science · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image Enhancement Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.2104.09068
CVPR 2021
arxiv created 2021/04/19 · openalex publication_date 2021/04/19 · arxiv updated 2021/04/20 · openalex created_date 2021/04/26 · openalex updated_date 2026/07/28
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.