A Learning Based Single Image Super Resolution Method Using Directionlets

In this paper, a novel directionally adaptive, learning based, single image super resolution method using multiple direction wavelet transform, called Directionlets is presented. The property of Directionlets to efficiently capture directional features and to extract edge information along different directions is used here to super resolve an image. The Directionlet coefficients at finer scales of the unknown high-resolution image are learned locally from a set of high-resolution training images and the inverse Directionlet transform recovers the super-resolved image. The experiments show that the proposed approach outperforms standard interpolation techniques like Cubic spline interpolation as well as standard Wavelet-based learning, both visually and in terms of the mean squared error (mse) values.