Image Inpainting Using Edge Structure Aware Hierarchical Guidance
Yashi Su, Lihong Ma, Jing Tian · 2021
Image inpainting is a challenging task to recover the missing visual information of an image in a visually plausible way. To tackle this challenge, this paper proposes a new image in-painting approach that employs the edge structure knowledge to hierarchically guide image completion. The hierarchical guidance consists of the following two key components. First, a new edge structure aware fusion is proposed to encourage information fusion between image feature maps and edge structure feature maps hierarchically via a channel-wise attention mechanism. Second, a novel edge structure aware loss is proposed, with the help of the conventional image loss together, to reconstruct the refined image via hierarchically supervising side outputs from the image completion network. Extensive experimental results are provided to demonstrate that the proposed approach reconstructs images with better quantitative quality and better qualitative performance using two benchmark image datasets.