Deep Face Deblurring

2017 ◽  
Author(s):  
Grigorios G. Chrysos ◽  
Stefanos Zafeiriou
Keyword(s):  
Face Deblurring

scholarly journals Deep Semantic Face Deblurring

2018 ◽  
Author(s):  
Ziyi Shen ◽  
Wei-Sheng Lai ◽  
Tingfa Xu ◽  
Jan Kautz ◽  
Ming-Hsuan Yang
Keyword(s):  
Face Deblurring

scholarly journals A multi-task approach to face deblurring

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Ziyi Shen ◽  
Tingfa Xu ◽  
Jizhou Zhang ◽  
Jie Guo ◽  
Shenwang Jiang
Keyword(s):  
Face Deblurring

scholarly journals Learning to Deblur Face Images via Sketch Synthesis

2020 ◽  
Vol 34 (07) ◽  
pp. 11523-11530 ◽  
Author(s):  
Songnan Lin ◽  
Jiawei Zhang ◽  
Jinshan Pan ◽  
Yicun Liu ◽  
Yongtian Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Domain Knowledge ◽  
Motion Blur ◽  
Deep Convolutional Neural Networks ◽  
Face Images ◽  
Restoration Algorithm ◽  
Deep Cnn ◽  
Blur Estimation ◽  
Face Deblurring

The success of existing face deblurring methods based on deep neural networks is mainly due to the large model capacity. Few algorithms have been specially designed according to the domain knowledge of face images and the physical properties of the deblurring process. In this paper, we propose an effective face deblurring algorithm based on deep convolutional neural networks (CNNs). Motivated by the conventional deblurring process which usually involves the motion blur estimation and the latent clear image restoration, the proposed algorithm first estimates motion blur by a deep CNN and then restores latent clear images with the estimated motion blur. However, estimating motion blur from blurry face images is difficult as the textures of the blurry face images are scarce. As most face images share some common global structures which can be modeled well by sketch information, we propose to learn face sketches by a deep CNN so that the sketches can help the motion blur estimation. With the estimated motion blur, we then develop an effective latent image restoration algorithm based on a deep CNN. Although involving the several components, the proposed algorithm is trained in an end-to-end fashion. We analyze the effectiveness of each component on face image deblurring and show that the proposed algorithm is able to deblur face images with favorable performance against state-of-the-art methods.

scholarly journals Progressive Semantic Face Deblurring

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223548-223561
Author(s):  
Tae Bok Lee ◽  
Soo Hyun Jung ◽  
Yong Seok Heo
Keyword(s):  
Face Deblurring
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