A Super-Resolution Algorithm Based on Total Variation

a novel algorithm for super resolution based on total variation prior and variational distribution approximations is proposed in this paper.We formulate the problem using a hierarchical Bayesian model where the reconstructed high resolution image and the model parameters are estimated simultaneously from the low resolution observations.The algorithm resulting from this formulation utilized variational inference and provides approximations to the posterior distributions of the latent variables.Due to the simultaneous parameter estimation, the algorithm is fully automated so parameter tuning is not required.Experimental results show that the proposed algorithm outperforms some of the state-of-the-art super resolution algorithms.