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- and high-resolution image patches. The experimental results demonstrate the effectiveness of the proposed algorithm by comparing with other state-of-the-art algorithms.
['Yi Tang', 'Yuan Yuan', 'Pingkun Yan', 'Xuelong Li']
Single-Image Super-Resolution via Sparse Coding Regression