Please use this identifier to cite or link to this item:
Title: DeepDeblur : text image recovery from blur to sharp
Authors: Mei, Jianhan
Wu, Ziming
Chen, Xiang
Qiao, Yu
Ding, Henghui
Jiang, Xudong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Mei, J., Wu, Z., Chen, X., Qiao, Y., Ding, H. & Jiang, X. (2019). DeepDeblur : text image recovery from blur to sharp. Multimedia Tools and Applications, 78(13), 18869-18885.
Journal: Multimedia Tools and Applications
Abstract: Digital images could be degraded by a variety of blur during the image acquisition (i.e. relative motion of cameras, electronic noise, capturing defocus, and so on). Blurring images can be computationally modeled as the result of a convolution process with the corresponding blur kernel and thus, image deblurring can be regarded as a deconvolution operation. In this paper, we explore to deblur images by approximating blind deconvolutions using a deep neural network. Different deep neural network structures are investigated to evaluate their deblurring capabilities, which contributes to the optimal design of a network architecture. It is found that shallow and narrow networks are not capable of handling complex motion blur. We thus, present a deep network with 20 layers to cope with text image blur. In addition, a novel network structure with Sequential Highway Connections (SHC) is leveraged to gain superior convergence. The experiment results demonstrate the state-of-the-art performance of the proposed framework with the higher visual quality of the delurred images.
ISSN: 1380-7501
DOI: 10.1007/s11042-019-7251-y
Rights: © 2019 Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Page view(s)

Updated on May 17, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.