High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling

Understand

Existing image inpainting methods often produce artifacts when dealing with large holes in real applications.

• To address this challenge, we propose an iterative inpainting method with a feedback mechanism.
• Specifically, we introduce a deep generative model which not only outputs an inpainting result but also a corresponding confidence map.
• Using this map as feedback, it progressively fills the hole by trusting only high-confidence pixels inside the hole at each iteration and focuses on the remaining pixels in the next iteration.