-SR: Laplacian Regularized Low-Rank Sparse Representation for Single Image Super-Resolution.
Wenming YangXuesen ShangKaiquan ChenShuifa SunPublished in: BigMM (2018)
Keyphrases
- single image super resolution
- sparse representation
- sparse coding
- low rank
- high dimensional data
- sparsity constraints
- dictionary learning
- norm minimization
- linear combination
- low rank matrix recovery
- convex optimization
- matrix completion
- missing data
- matrix factorization
- image patches
- singular value decomposition
- low rank matrix
- high order
- compressive sensing
- image classification
- face recognition
- dimensionality reduction
- nearest neighbor
- semi supervised
- negative matrix factorization
- least squares
- data sets
- random projections
- low dimensional
- data points
- image super resolution
- high dimensional
- signal processing
- image representation
- pattern recognition
- small number
- original data
- test images
- optical flow
- data analysis
- generative model
- video sequences
- training data