Global and local attention-based free-form image inpainting

10Citations
Citations of this article
28Readers
Mendeley users who have this article in their library.

Abstract

Deep-learning-based image inpainting methods have shown significant promise in both rectangular and irregular holes. However, the inpainting of irregular holes presents numerous challenges owing to uncertainties in their shapes and locations. When depending solely on convolutional neural network (CNN) or adversarial supervision, plausible inpainting results cannot be guaranteed because irregular holes need attention-based guidance for retrieving information for content generation. In this paper, we propose two new attention mechanisms, namely a mask pruning-based global attention module and a global and local attention module to obtain global dependency information and the local similarity information among the features for refined results. The proposed method is evaluated using state-of-the-art methods, and the experimental results show that our method outperforms the existing methods in both quantitative and qualitative measures.

Cite

CITATION STYLE

APA

Nadim, U. S. M., & Jung, Y. J. (2020). Global and local attention-based free-form image inpainting. Sensors (Switzerland), 20(11), 1–27. https://doi.org/10.3390/s20113204

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free