NDLI: Non-uniform image blind deblurring by two-stage fully convolution network

Content Provider	IET Digital Library
Author	Wu, Chudan Wo, Yan Han, Guoqing Wu, Zhangyong Liang, Jiyun
Abstract	Deep neural networks have recently demonstrated high performance for deblurring. However, few methods are designed for both non-uniform image blur estimation and removal with highly efficient. In this study, the authors proposed a fully convolutional network that outputs estimated blur and restored image in one feed-forward pass for the non-uniformly blurred image of any input-size. The proposed network contains two subnets. The parameter estimation subnet P-net predicts pixel-wise parameters of multiple blur types with high accuracy. The output of P-net is used as a condition, which guides the blur removal subnet G-net to restore a high quality latent sharp image. P-net and G-net are ultimately integrated into a single framework called PG-net, which guarantees the consistency of parameter estimation and blur removal, thereby improves algorithm efficiency. Experiment results show that the authors blur parameter estimation method as well as their deblurring method outperforms the comparison methods both quantitatively and qualitatively.
Starting Page	2588
Ending Page	2596
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 11, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5716
Journal	IET Image Processing
Publisher Date	2020-04-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Blur Removal Subnet G-net Blur Restored Image Computer Vision And Image Processing Technique Deblurring Method Deep Neural Network Feed-forward Pass Fully Convolutional Network High Quality Latent Sharp Image Image Restoration Multiple Blur Types Neural Nets Nonuniform Image Blind Deblurring Optical, Image And Video Signal Processing Parameter Estimation Parameter Estimation Method Parameter Estimation Subnet P-net PG-net Pixel-wise Parameter Statistics Two-stage Fully Convolution Network
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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