Multi-morphology image super-resolution via sparse representation
An image super-resolution algorithm based on morphological diversity is proposed. The learning mechanism relies on sparse representation. Dictionary training algorithm improves accuracy and efficiency for representation. An effectively sampling by variance is adopted to construct training set. Good performance can be achieved at low dictionary size and patch size.
super-resolution sparse representation dictionary training morphological component analysis