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.
2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA)