Sparsity-driven reconstruction of ℓ∞-decoded images
Li Y.; Zhou J.
Conference NameIEEE International Conference on Image Processing (ICIP)
Source Publication2014 IEEE International Conference on Image Processing, ICIP 2014
Conference DateOCT 27-30, 2014
Conference PlaceParis, FRANCE

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
URLView the original
Indexed BySCI
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology
WOS SubjectComputer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS IDWOS:000370063604157
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Li Y.,Zhou J.. Sparsity-driven reconstruction of ℓ∞-decoded images[C],2014:4612-4616.
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