Adaptive regularised l2‐boosting on clustered sparse coefficients for single image super‐resolution - Yulan Han; Yongping 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 l2‐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 l2‐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.