In this paper, a new directionally adaptive, learning based, single image super resolution method using
multiple direction wavelet transform, called Directionlets is presented. This method uses directionlets to
effectively capture directional features and to extract edge information along different directions of a set of
available high resolution images .This information is used as the training set for super resolving a low
resolution input image and the Directionlet coefficients at finer scales of its high-resolution image are
learned locally from this training set and the inverse Directionlet transform recovers the super-resolved
high resolution image. The simulation results showed 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. This method gives good result with aliased
images also.
Description:
International Journal of Artificial Intelligence & Applications (IJAIA), Vol.1, No.4, October 2010