Manifold-preserving single-image super-resolution based on collaborative representation support

Nowadays, super-resolution is becoming more and more important in most optical imaging systems and image processing applications due to current resolution limit of charged couple device (CCD) and complementary metal-oxide semiconductor (CMOS). In this paper, we proposed a novel single-image super-resolution algorithm, which combines collaborative representation into manifold-preserving approach. The main contributions of our work can be summarized into two points. First, supporting bases which are used to calculate the mapping relationship between low-resolution (LR) images and high-resolution (HR) images are obtained by applying collaborative representation on the neighborhoods of the dictionary atoms, where the neighborhoods are clustered from the whole training sample pools. Second, to achieve a balance between execution speed and reconstruction quality, a global solution for our framework is constructed by transferring the online calculating process to offline. We demonstrate better results on commonly used datasets, showing both better visual performance and higher index values compared to other methods.