Image super resolution based on couple dictionary learning

A novel coupled dictionary training method for single image super resolution based on patch-wise sparse recovery is proposed,where the learned couple dictionaries relate the low and high-resolution image patch spaces via sparse representation.The learning process enforces that the sparse representation of a low-resolution image patch in terms of the low-resolution dictionary can well reconstruct its underlying high-resolution image patch with the dictionary in the high-resolution image patch space.The learning problem is modeled as a bilevel optimization problem,where the optimization includes an l1 norm minimization problem in its constraints.Implicit differentiation is employed to calculate the desired gradient for stochastic gradient descent.Coupled dictionary learning method can outperform the existing joint dictionary training method both quantitatively and qualitatively.Extensive experimental comparisons with state-of-the-art super-resolution algorithms validate the effectiveness of our proposed approach.