title: A statistical prediction model based on sparse representations for single image super-resolution.
abstract: We address single image super-resolution using a statistical prediction model based on sparse representations of low- and high-resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via MMSE estimation and the resulting scheme has the useful interpretation of a feedforward neural network. To further enhance performance, we suggest data clustering and cascading several levels of the basic algorithm. We suggest a training scheme for the resulting network and demonstrate the capabilities of our algorithm, showing its advantages over existing methods based on a low- and high-resolution dictionary pair, in terms of computational complexity, numerical criteria, and visual appearance. The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution.
authors.affiliation: Department of Electrical Engineering, Technion¿Israel Institute of Technology, Haifa, Israel.
authors.forename: Tomer
authors.initials: T
authors.lastname: Peleg
authors.affiliation: Computer Science Department, Technion¿Israel Institute of Technology, Haifa, Israel.
authors.forename: Michael
authors.initials: M
authors.lastname: Elad