Sampling and Reconstruction of Sparse Signals in Shift-Invariant Spaces: Generalized Shannon's Theorem Meets Compressive Sensing.
Tin VlasicDamir SersicPublished in: CoRR (2020)
Keyphrases
- compressive sensing
- shift invariant
- signal reconstruction
- compressive sampling
- sparse coding
- sparse representation
- signal processing
- random projections
- wavelet domain
- rotation invariant
- wavelet coefficients
- image representation
- discrete wavelet transform
- compressed sensing
- orthogonal matching pursuit
- image classification
- blurred images
- random sampling
- computer vision
- wavelet transform
- edge preserving
- pattern recognition
- multiscale
- image processing
- image sequences
- object recognition
- image retrieval
- dimensionality reduction
- linear combination