Learning Face Deblurring Fast and Wide

Jin, Meiguang; Hirsch, Michael; Favaro, Paolo (June 2018). Learning Face Deblurring Fast and Wide. In: New Trends in Image Restoration and Enhancement workshop, Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City, Utah, United States. June 18, 2018.

[img] Text
Jin_Learning_Face_Deblurring_CVPR_2018_paper.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (2MB) | Request a copy

Portrait images and photos containing faces are ubiquitous on the web and the predominant subject of images shared via social media. Especially selfie images taken with lightweight smartphone cameras are susceptible to camera shake. Despite significant progress in the field of image deblurring over the last decade, the performance of stateof-the-art deblurring methods on blurry face images is still limited. In this work, we present a novel deep learning architecture that is designed to be computationally fast and exploits a very wide receptive field to return sharp face images even in challenging scenarios. Our network features an effective resampling convolution operation that ensures a wide receptive field from the very first layers, while at the same time being highly computationally efficient. We also show that batch normalization prevents networks from yielding high-quality image results and introduce instance normalization instead. We demonstrate our architecture on face deblurring as well as other more general scenes. Extensive experiments with state-of-the-art methods demonstrate the effectiveness of our proposed network, in terms of run-time, accuracy, and robustness to ISO levels as well as gamma correction.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF)

UniBE Contributor:

Jin, Meiguang and Favaro, Paolo

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics

Language:

English

Submitter:

Xiaochen Wang

Date Deposited:

29 May 2019 10:30

Last Modified:

26 Oct 2019 15:46

BORIS DOI:

10.7892/boris.126538

URI:

https://boris.unibe.ch/id/eprint/126538

Actions (login required)

Edit item Edit item
Provide Feedback