Single-Image Super-Resolution via an Iterative Reproducing Kernel Hilbert Space Method

Image super-resolution (SR), a process to enhance image resolution, has important applications in satellite imaging, high-definition television, medical imaging, and so on. Many existing approaches use multiple low-resolution (LR) images to recover one high-resolution (HR) image. In this paper, we present an iterative scheme to solve single-image SR problems. It recovers a high-quality HR image from solely one LR image without using a training data set. We solve the problem from image intensity function estimation perspective and assume that the image contains smooth and edge components. We model the smooth components of an image using a thin-plate reproducing kernel Hilbert space and the edges using approximated Heaviside functions. The proposed method is applied to image patches, aiming to reduce computation and storage. Visual and quantitative comparisons with some competitive approaches show the effectiveness of the proposed method.

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