Abstract
This paper presents a support vector data description (SVDD) based adaptive resolution enhancement of still images. First, the proposed method estimates unknown high-frequency components of a target image in such a way that the distance between the estimation result and the center of hypersphere obtained from training high-resolution (HR) local images by the SVDD becomes minimum. In this scheme, since local images between two different levels of a pyramid structure are similar to each other, local images within the target low resolution (LR) image are utilized as the training data. Furthermore, in order to realize accurate resolution enhancement of images containing several kinds of textures, these training local images are previously clustered, and the above estimation scheme is performed by using the hypersphere of each cluster. In this procedure, the proposed method determines the final output based on the probability defined from the distance between the estimation result and the center of the hypersphere for each cluster. Therefore, this approach provides a solution to a problem of traditional methods of not being able to perform adaptive estimation of the high-frequency components, and accurate resolution enhancement can be expected by our method. Some experimental results are shown to verify the performance of the proposed method.
