A Comparison of Feature-Combination for Example-Based Super Resolution

Abstract

[[abstract]]Super resolution (SR) in computer vision is an important task. In this paper, we compared several common used features in image super resolution of example-based algorithms. To combine features, we develop a cascade framework to both solve the problem of deciding weights among features and to improve computation efficiency. Finally, we modify the framework to have an adaptive threshold such that not only the computation load is much reduced but the modified framework is suitable to any query image as well as various image databases.[[sponsorship]]the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)[[conferencetype]]國際[[conferencedate]]2014, 5/13 - 5/16[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tainan, Taiwa