Single image super-resolution via learned representative features and sparse manifold embedding

Advances in machine learning technology have made efficient Super-Resolution Image Reconstruction (SRIR) possible. In this paper, we advance a hierarchical support vector machine (HSVM) to learn representative features of both training and test Low-Resolution (LR) image patches. Then a sparse manifold assumption is cast on training patch features to find local HR neighbors for each test LR input. The reconstructed High-Resolution (HR) patches can then be derived via Neighbors Embedding (NE) technology with the help of the HR neighbors from training HR patches, and compensated for the LR images. Some experiments are taken on realizing a 3X amplification of natural images, the recovered results prove its efficiency and superiority to its counterparts visually and qualitatively.