NDLI: Deep-network based method for joint image deblocking and super-resolution

Content Provider	IET Digital Library
Author	Li, Xiaoguang Sun, Xu Lam, Kin Man Zhuo, Li Li, Jiafeng Dong, Ning
Abstract	Many pieces of research have been conducted on image-restoration techniques to recover high-quality images from their low-quality versions, but they usually aim to handle a single degraded factor. However, captured images usually suffer from various degradation factors, such as low resolution and compression distortion, in the procedures of image acquisition, compression, and transmission simultaneously. Ignoring the correlation of different degraded factors may result in the limited efficiency of the existing image-restoration methods for captured images. A joint deep-network-based image-restoration algorithm is proposed to establish a restoration framework for image deblocking and super-resolution. The proposed convolutional neural network is made up of two stages. A deblocking network is constructed with two cascade deblocking subnets first, then, super-resolution is performed by a very deep network with skipping links. Cascading these two stages forms a novel deep network. An end-to-end training scheme is developed, which makes the two stages be trained jointly so as to achieve better performance. Intensive evaluations have been conducted to measure the performance of the authors’ method both in general images and face images. Experimental results on several datasets demonstrate that the proposed method outperforms other state-of-the-art methods, in terms of both subjective and objective performances.
Starting Page	1636
Ending Page	1647
Page Count	12
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 10, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6113
Journal	IET Image Processing
Publisher Date	2019-04-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Cascade Deblocking Subnets Compression Distortion Computer Vision And Image Processing Technique Convolutional Neural Network Deblocking Network Deep Network Deep-network Based Method Degradation Factors End-to-end Training Scheme Face Image General Image High-quality Image Image Acquisition Image Resolution Image Restoration Joint Deep-network-based Image-restoration Algorithm Joint Image Deblocking Low-quality Versions Neural Computing Technique Neural Nets Optical, Image And Video Signal Processing Single Degraded Factor Super-resolution
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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