Producible Kernel Hilbert Space and Heaviside Functions in Image EnhancementMS49

In this talk, we first present an iterative scheme to solve single image super-resolution problems. It recovers a high quality high-resolution image from solely one low-resolution image without using a training data set. We solve the problem from image intensity function estimation perspective and assume the image contains smooth and edge components. We model the smooth components of an image using a thin-plate reproducing kernel Hilbert space (RKHS) and the edges using approximated Heaviside functions. The proposed method is applied to image patches, aiming to reduce computation and storage. In addition, we also extend the proposed framework to the fusion of panchromatic and multispectral images. Visual and quantitative comparisons with some competitive approaches show the effectiveness of the proposed framework.

This presentation is part of Minisymposium “MS49 - Image Restoration, Enhancement and Related Algorithms (4 parts)
organized by: Weihong Guo (Case Western Reserve University) , Ke Chen (University of Liverpool) , Xue-Cheng Tai (Hong Kong Baptist University) , Guohui Song (Clarkson University) .

Liang-Jian Deng (University of Electronic Science and Technology of China)
image enhancement, image reconstruction