NEIGHBORHOOD ISSUE IN SINGLE-FRAME IMAGE SUPER-RESOLUTION (FriAmPO1)

Author(s) : Xu Su (University of Texas at San Antonio, United States of America) Qi Tian (University of Texas at San Antonio, United States of America) Qing Xue (University of Texas at San Antonio, United States of America) Nicu Sebe (University of Amsterdam, Netherlands) Jingsheng Ma (Heriot-Watt University, United Kingdom)

Abstract : Super-Resolution is the problem of generating one or a set of high-resolution images from one or a sequence of low-resolution frames. Most methods have been proposed for super-resolution based on multiple low resolution images of the same scene, which is called multiple-frame super-resolution. Only a few approaches produce a high-resolution image from a single low-resolution image, with the help of one or a set of training images from scenes of the same or different types. It is referred to as single-frame super-resolution. This article reviews a variety of single-frame Super-Resolution methods proposed in the recent years. In the paper, a new manifold learning method: locally linear embedding (LLE) and its relation with single-frame super-resolution is introduced. Detailed study of a critical issue: “Neighborhood Issue” is presented with related experimental results and analysis. And possible future research is given.

Menu