Single-image Super-resolution via De-biased Sparse Representation

Sparse representation and dictionary learning of image patches are well-known methods for single-image super-resolution. However, due to the regularization term of sparse-inducing penalties, the solution is usually biased. In this study, we present a de-biasing framework by adding a de-biasing step after sparse representation. Two de-biasing methods with sign consistency and feature consistency are further proposed under this framework. Using a unified proximal gradient method, we can solve the proposed de-biasing methods efficiently. Experiments on real super-resolution datasets validate the effectiveness and robustness of the proposed de-biasing methods.