A convex formulation for hyperspectral image superresolution via subspace-based regularization.
Miguel SimõesJosé M. Bioucas-DiasLuís B. AlmeidaJocelyn ChanussotPublished in: CoRR (2014)
Keyphrases
- convex formulation
- super resolution
- hyperspectral images
- spectral signatures
- convex optimization
- hyperspectral
- high resolution
- potts model
- remote sensing
- gradient method
- image restoration
- image reconstruction
- loss function
- multispectral
- motion estimation
- high quality
- convex relaxation
- low dimensional
- markov random field
- target detection
- motion blur
- light field
- infrared
- total variation
- principal component analysis
- feature space
- frequency domain
- remote sensing images
- high dimensional
- spatial resolution
- dimensionality reduction
- image data
- image processing
- high dimensional data
- graph cuts
- multispectral images
- pairwise
- feature extraction