Super-Resolution through Neighbor Embedding

In this paper, we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its highresolution counterpart using a set of training examples. While this formulation resembles other learningbased methods for super-resolution, our method has been inspired by recent manifold learning methods, particularly locally linear embedding (LLE). Specifically, small image patches in the low- and high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is very flexible and gives good empirical resu...

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Hong Chang, Dit-Yan Yeung, Yimin Xiong
• Keywords: Computer Vision, CVPR 2004, Distinct Feature Spaces, Manifold Learning Methods, Similar Local Geometry, Small Image Patches, Training Image Pairs