Modified dictionary learning method for sparsity based single image super-resolution

This paper proposes a self-learning based dictionary training approach for single image super-resolution where the low-resolution (LR) and high-resolution (HR) dictionaries are jointly trained using training data set and different scaled versions of the input image. Local variance based salient feature identification is carried out to speed up the super-resolution algorithm. It can be demonstrated that our modified SR algorithm can qualitatively and quantitatively outperform bicubic interpolation and state-of-the-art methods.