Semi-Sparsity for Smoothing Filters
Author:
Affiliation:
1. Department of Mathematics: Analysis, Logic and Discrete Mathematics, Ghent University, Ghent, Belgium
2. School of Mathematical Sciences, Beihang University (BUAA), Beijing, China
Funder
Research Foundation–Flanders (FWO) Odysseus 1
Methusalem Programme of the Ghent University Special Research Fund
National Science and Technology Major Project, China
Engineering and Physical Sciences Research Council
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Graphics and Computer-Aided Design,Software
Link
http://xplorestaging.ieee.org/ielx7/83/9991910/10054601.pdf?arnumber=10054601
Reference58 articles.
1. Fast Global Image Smoothing Based on Weighted Least Squares
2. NIMA: Neural Image Assessment
3. Nonlinear total variation based noise removal algorithms
4. A Feature-Enriched Completely Blind Image Quality Evaluator
5. Sparse norm filtering;ye;arXiv 1305 3971,2013
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