Kernel PCA-based resolution enhancement approach of still images using different levels of pyramid structure

This paper presents a kernel PCA-based adaptive resolution enhancement method of still images. The proposed method introduces two novel approaches into the kernel PCA-based reconstruction of high-frequency components missed from a high-resolution (HR) image. First, since local images between two different resolution levels of a pyramid structure are similar to each other, nonlinear eigenspaces of local images in the target low-resolution (LR) image are utilized as those of local images in the HR image. Further, in the kernel PCA-based reconstruction process of the high-frequency components, our method monitors errors caused in the known low-frequency components and realizes the selection of the optimal eigenspace. Then, since the missing high-frequency components can be adaptively estimated, the accurate HR image can be obtained.