KPLS-based image super-resolution using clustering and weighted boosting

Kernel partial least squares (KPLS) algorithm for super-resolution (SR) has carried out a regression model to estimate a high-resolution (HR) feature patch from its corresponding low-resolution (LR) feature patch using a training database. However, KPLS may be time-consuming in the neighbor search and use of principal components. In this paper we propose a clustering and weighted boosting (CWB) framework to improve the efficiency in KPLS regression model construction without reducing SR reconstruction quality. First, the training LR–HR feature patch pairs are divided into a certain number of clusters. For each test LR feature patch, the neighbor search in the selected cluster saves more computational costs than that in the whole training database. Second, a weighted boosting scheme is used to adaptively construct the KPLS regression model with the best number of principal components (BNPC). Experimental results on natural scene images suggest that the proposed CWB method can effectively improve the efficiency of KPLS-based SR method while preserving reconstruction quality, and achieve better performance than the conventional KPLS method.