UM  > 科技學院  > 電腦及資訊科學系
Sparsity-driven reconstruction of ℓ∞-decoded images
Li Y.; Zhou J.
2014-01-28
Conference NameIEEE International Conference on Image Processing (ICIP)
Source Publication2014 IEEE International Conference on Image Processing, ICIP 2014
Pages4612-4616
Conference DateOCT 27-30, 2014
Conference PlaceParis, FRANCE
Abstract

In this paper, we propose a sparsity-driven restoration technique to improve the coding performance of the ℓ-decoded images. This is achieved by incorporating a ℓ minimization term into a ℓ optimization framework, where the weighting vectors balancing the relative contribution of each term are appropriately determined. The ℓ constraints inherent to ℓ-constrained predictive coding are also included to narrow the solution space, leading to more accurate estimation. Experimental results show that our proposed scheme significantly improves the ℓ performance of the ℓ-decoded images, while still preserving a tight error bound on every single pixel. In addition, when comparing with the existing scheme of restoring the ℓ-decoded images, the PSNR gain can be up to 1 dB.

KeywordImage Restoration Sparse Representation ℓ∞-constrained Predictive Coding
DOI10.1109/ICIP.2014.7025935
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS IDWOS:000370063604157
Fulltext Access
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Li Y.,Zhou J.. Sparsity-driven reconstruction of ℓ∞-decoded images[C],2014:4612-4616.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li Y.]'s Articles
[Zhou J.]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li Y.]'s Articles
[Zhou J.]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li Y.]'s Articles
[Zhou J.]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

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