Image Inpainting with External-internal Learning and Monochromic Bottleneck
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.
Code (1)
Tasks
Image InpaintingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Test-Time Adaptation for Out-of-distributed Image Inpainting
Deep learning-based image inpainting algorithms have shown great performance via powerful learned prior from the numerous external natural images. However, they show unpleasant results on the test image whose distributio…
Image InpaintingTest-time AdaptationvalidPolyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls
We propose Polyffusion, a diffusion model that generates polyphonic music scores by regarding music as image-like piano roll representations. The model is capable of controllable music generation with two paradigms: inte…
Music GenerationRestore from Restored: Single-image Inpainting
Recent image inpainting methods show promising results due to the power of deep learning, which can explore external information available from a large training dataset. However, many state-of-the-art inpainting networks…
Image InpaintingAn Internal Learning Approach to Video Inpainting
We propose a novel video inpainting algorithm that simultaneously hallucinates missing appearance and motion (optical flow) information, building upon the recent 'Deep Image Prior' (DIP) that exploits convolutional netwo…
Optical Flow EstimationVideo InpaintingRestore from Restored: Single-image Inpainting
Recent image inpainting methods have shown promising results due to the power of deep learning, which can explore external information available from the large training dataset. However, many state-of-the-art inpainting …
Image Inpainting