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In this paper, a new directionally adaptive, learning based, single imagesuper resolution method using multiple direction wavelet transform, calledDirectionlets is presented. This method uses directionlets to effectivelycapture directional features and to extract edge information along differentdirections of a set of available high resolution images .This information isused as the training set for super resolving a low resolution input image andthe Directionlet coefficients at finer scales of its high-resolution image arelearned locally from this training set and the inverse Directionlet transformrecovers the super-resolved high resolution image. The simulation resultsshowed that the proposed approach outperforms standard interpolation techniqueslike Cubic spline interpolation as well as standard Wavelet-based learning,both visually and in terms of the mean squared error (mse) values. This methodgives good result with aliased images also.

Single Frame Image super Resolution using Learned Directionlets