Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74197
Title: A joint deep-network-based image restoration algorithm for multi-degradations
Authors: Sun, X
Li, X
Zhuo, L
Lam, KM 
Li, J
Keywords: Image restoration
Joint deep network
Multi-degradations
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: Proceedings - IEEE International Conference on Multimedia and Expo, 2017, 8019361, p. 301-306 How to cite?
Abstract: In the procedures of image acquisition, compression, and transmission, captured images usually suffer from various degradations, such as low-resolution and compression distortion. Although there have been a lot of research done on image restoration, they usually aim to deal with a single degraded factor, ignoring the correlation of different degradations. To establish a restoration framework for multiple degradations, a joint deep-network-based image restoration algorithm is proposed in this paper. The proposed convolutional neural network is composed of two stages. Firstly, a de-blocking subnet is constructed, using two cascaded neural network. Then, super-resolution is carried out by a 20-layer very deep network with skipping links. Cascading these two stages forms a novel deep network. Experimental results on the Set5, Setl4 and BSD100 benchmarks demonstrate that the proposed method can achieve better results, in terms of both the subjective and objective performances.
URI: http://hdl.handle.net/10397/74197
ISBN: 9781509060672
ISSN: 1945-7871
DOI: 10.1109/ICME.2017.8019361
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

47
Last Week
0
Last month
Citations as of Jul 16, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.