Adaptive regularised l 2 ‐boosting on clustered sparse coefficients for single image super‐resolution
Yulan Han, Yong-Ping Zhao, Haifeng Yu · IET Computer Vision · 2017
In this study, the authors propose a novel approach for single image super‐resolution. Their method is based on the idea of learning a mapping function, which can reveal the intrinsic relationship between sparse coefficients of low‐resolution (LR) and high‐resolution (HR) image patch pairs with respect to their individual dictionaries. Adaptive regularised l 2 ‐boosting algorithm is proposed to learn this type of mapping function. Specifically, to reduce time consumption, the authors cluster training patches into several clusters. Within each cluster, a pair of dictionaries for LR and HR image patches is jointly trained. Adaptive regularised l 2 ‐boosting algorithm is then employed to obtain the function. Thus, in a reconstruction stage, for each given input LR image patch, the authors can effectively estimate its corresponding HR image patch. Their extensive experimental results demonstrated that the proposed method achieves a performance of similar quality performance to that of the top methods.