  Recently, learning-based algorithms for image inpainting achieve remarkable
progress dealing with squared or irregular holes. However, they fail to
generate plausible textures inside damaged area because there lacks surrounding
information. A progressive inpainting approach would be advantageous for
eliminating central blurriness, i.e., restoring well and then updating masks.
In this paper, we propose full-resolution residual network (FRRN) to fill
irregular holes, which is proved to be effective for progressive image
inpainting. We show that well-designed residual architecture facilitates
feature integration and texture prediction. Additionally, to guarantee
completion quality during progressive inpainting, we adopt N Blocks, One
Dilation strategy, which assigns several residual blocks for one dilation step.
Correspondingly, a step loss function is applied to improve the performance of
intermediate restorations. The experimental results demonstrate that the
proposed FRRN framework for image inpainting is much better than previous
methods both quantitatively and qualitatively. Our codes are released at:
\url{https://github.com/ZongyuGuo/Inpainting_FRRN}.
