Generative image inpainting with residual attention learning

Fang Wan; Yuesheng Zhu; Zehua Cai

doi:10.1117/12.2573109

12 June 2020 Generative image inpainting with residual attention learning

Fang Wan, Yuesheng Zhu, Zehua Cai

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 115190O (2020) https://doi.org/10.1117/12.2573109
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

Recent deep learning approaches have shown significant improvements in the challenging task of image inpainting. However, these methods may generate blurry output and distorted textures. In this paper, we propose an efficient end-to-end two-stage network for image inpainting. In the coarse stage, we employ residual dense block (RDB) as well as short and long skip connections to fully leverage and exploit features from all convolutional layers and give a globally rough reconstruction. In the refinement stage, we propose a local and global residual network based on channel and spatial attention block (CSAB) to adaptively weigh both channel-wise and spatial-wise features focusing on more meaningful information, and generate a locally fine-detailed image. Experiments on Paris StreetView and DTD textures demonstrate the effectiveness and efficiency of our method. Results show that our method outperforms the baseline techniques quantitatively and qualitatively.

Citation Download Citation

Fang Wan, Yuesheng Zhu, and Zehua Cai "Generative image inpainting with residual attention learning", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 115190O (12 June 2020); https://doi.org/10.1117/12.2573109

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Visualization

Convolution

Computer programming

Network architectures

Binary data

Gallium nitride

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years