An l1/2-BTV Regularization Algorithm for Super-resolution

In this paper, we propose a novelregularization term for super-resolution by combining a bilateral total variation (BTV) regularizer and a sparsity prior model on the image. The term is composed of the weighted least squares minimization and the bilateral filter proposed by Elad, but adding an l(1/2) regularizer. It is referred to as l(1/2)-BTV. The proposed algorithm serves to restore image details more precisely and eliminate image noise more effectivelyby introducing the sparsity of the l(1/2) regularizer into the traditional bilateral total variation (BTV) regularizer. Experiments were conducted on both simulated and real image sequences. The results show that the proposed algorithm generates high-resolution images of better quality, as defined by both de-noising and edge-preservation metrics, than other methods.