Adaptive Single Image Superresolution Approach Using Support Vector Data Description

An adaptive single image superresolution (SR) method using a support vector data description (SVDD) is presented. The proposed method represents the prior on high-resolution (HR) images by hyperspheres of the SVDD obtained from training examples and reconstructs HR images from low-resolution (LR) observations based on the following schemes. First, in order to perform accurate reconstruction of HR images containing various kinds of objects, training HR examples are previously clustered based on the distance from a center of a hypersphere obtained for each cluster. Furthermore, missing high-frequency components of the target image are estimated in order that the reconstructed HR image minimizes the above distances. In this approach, the minimized distance obtained for each cluster is utilized as a criterion to select the optimal hypersphere for estimating the high-frequency components. This approach provides a solution to the problem of conventional methods not being able to perform adaptive estimation of the high-frequency components. In addition, local patches in the target low-resolution (LR) image are utilized as the training HR examples from the characteristic of self-similarities between different resolution levels in general images, and our method can perform the SR without utilizing any other HR images.