Image Hallucination Using Neighbor Embedding over Visual Primitive Manifolds

In this paper, we propose a novel learning-based method for image hallucination, with image super-resolution being a specific application that we focus on here. Given a low-resolution image, its underlying higher-resolution details are synthesized based on a set of training images. In order to build a compact yet descriptive training set, we investigate the characteristic local structures contained in large volumes of small image patches. Inspired by recent progress in manifold learning research, we take the assumption that small image patches in the low-resolution and high-resolution images form manifolds with similar local geometry in the corresponding image feature spaces. This assumption leads to a super-resolution approach which reconstructs the feature vector corresponding to an image patch by its neighbors in the feature space. In addition, the residual errors associated with the reconstructed image patches are also estimated to compensate for the information loss in the local ...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Wei Fan, Dit-Yan Yeung
• Keywords: Computer Vision, Corresponding Image Feature, CVPR 2007, Image Hallucination, Image Super-resolution, Low-resolution Image, Small Image Patches