Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107228
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | en_US |
dc.creator | Sun, X | en_US |
dc.creator | Li, X | en_US |
dc.creator | Zhuo, L | en_US |
dc.creator | Lam, KM | en_US |
dc.creator | Li, J | en_US |
dc.date.accessioned | 2024-06-13T01:04:44Z | - |
dc.date.available | 2024-06-13T01:04:44Z | - |
dc.identifier.isbn | 978-1-5090-6067-2 (Electronic) | en_US |
dc.identifier.isbn | 978-1-5090-6066-5 (USB) | en_US |
dc.identifier.isbn | 978-1-5090-6068-9 (Print on Demand(PoD)) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107228 | - |
dc.description | 2017 IEEE International Conference on Multimedia and Expo (ICME), 10-14 July 2017, Hong Kong, China | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication X. Sun, X. Li, L. Zhuo, K. M. Lam and J. Li, "A joint deep-network-based image restoration algorithm for multi-degradations," 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China, 2017, pp. 301-306 is available at https://doi.org/10.1109/ICME.2017.8019361. | en_US |
dc.subject | Image restoration | en_US |
dc.subject | Joint deep network | en_US |
dc.subject | Multi-degradations | en_US |
dc.title | A joint deep-network-based image restoration algorithm for multi-degradations | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 301 | en_US |
dc.identifier.epage | 306 | en_US |
dc.identifier.doi | 10.1109/ICME.2017.8019361 | en_US |
dcterms.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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In Proceedings of 2017 IEEE International Conference on Multimedia and Expo (ICME), 10-14 July 2017, Hong Kong, China, p. 301-306 | en_US |
dcterms.issued | 2017 | - |
dc.identifier.scopus | 2-s2.0-85030216607 | - |
dc.relation.conference | IEEE International Conference on Multimedia and Expo [ICME] | en_US |
dc.description.validate | 202404 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0667 | - |
dc.description.fundingSource | Self-funded | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 9604178 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
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Lam_Joint_Deep-Network-Based_Image.pdf | Pre-Published version | 1.04 MB | Adobe PDF | View/Open |
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