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A universal variational framework for sparsity-based image inpainting
Fang Li, Tieyong ZENG
Department of Mathematics
Research output
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Contribution to journal
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Journal article
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peer-review
39
Citations (Scopus)
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Dive into the research topics of 'A universal variational framework for sparsity-based image inpainting'. Together they form a unique fingerprint.
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Keyphrases
Variational Framework
100%
Sparsity Basis
100%
Image Inpainting
100%
Denoising
66%
Tight Frame
33%
Wavelet Transform
33%
Numerical Experiments
33%
Operator Splitting Method
33%
L1-norm
33%
Alternating Minimization
33%
Original Problem
33%
Exact Solutions
33%
Pixel Value
33%
Regularization Operator
33%
Pixel Filling
33%
BM3D Filter
33%
Pixel Block
33%
Latent Image
33%
Theoretical Convergence
33%
Gradient Operator
33%
Inpainting
33%
Linear Combination
33%
Denoising Method
33%
Missing pixels
33%
Framelet Transform
33%
Numerical Algorithms
33%
Scratch Removal
33%
Text Removal
33%
Mathematics
Operator Splitting
100%
Subproblem
100%
Minimizes
100%
Numerical Experiment
100%
Regularization
100%
Slight Modification
100%
Wavelet Transform
100%
Tight Frame
100%
Linear Combination
100%
Framelets
100%
Numerical Algorithm
100%