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Learning an appropriate dictionary is a critical issue for sparse representation based super-resolution algorithms. A good dictionary can well represent an underlying image. In super-resolution algorithms, classical dictionary learning doesn't consider the information of test image which contains the information of the reconstructed image. In this paper, the new dictionary is design for image super-resolution,...
Supervised learning-based methods are popular in single-image super-resolution (SR), and the underlying idea is to learn a map from input low-resolution (LR) images to target high-resolution (HR) images based on a training set. The generalization of the learned map ensures the well performance of these methods on various test images. However, the universality of these methods weakens their specificity...
In this paper, it has been shown that the sparse coding algorithm for single-image super-resolution is equivalent to a linear regression algorithm in the sparse coding space. Following the idea, the sparse coding algorithm are generalized by a novel $L_{2}$-Boosting-based single-resolution super-resolution algorithm which focuses on the relationship between sparse codings corresponding to the low-...
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