Learning adaptive interpolation kernels for fast single-image super resolution

This paper presents a fast single-image superresolution approach that involves learning multiple adaptive interpolation kernels. Based on the assumptions that each high-resolution image patch can be sparsely represented by several simple image structures and that each structure can be assigned a suitable interpolation kernel, our approach consists of the following steps. First, we cluster the training image patches into several classes and train each class-specific interpolation kernel. Then, for each input lowresolution image patch, we select few suitable kernels of it to make up the final interpolation kernel. Since the proposed approach is mainly based on simple linear algebra computations, its efficiency can be guaranteed. And experimental comparisons with state-of-the-art super-resolution reconstruction algorithms on simulated and real-life examples can validate the performance of our proposed approach. This work was supported by the National Natural Science Foundation of China (61032007, 61101219, 61201375) and the National High Technology R&D Program of China (863 Program) (Grant No. 2013AA014602). X. Hu · S. Peng (B) Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China e-mail: [email protected] X. Hu e-mail: [email protected] W.-L. Hwang Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan, ROC e-mail: [email protected] W.-L. Hwang Department of Information Management, Kainan University, Taoyuan County 33857, Taiwan, ROC