Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution. (5th July 2007)

• Record Type: Journal Article
• Authors: Das Gupta Das Gupta, Mithun Mithun; Rajaram Rajaram, Shyamsundar Shyamsundar; Huang Huang, Thomas S. Thomas S.; Petrovic Petrovic, Nemanja Nemanja
• Other Names: Schultz Schultz Richard R. Richard R. Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2007-07-05
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/85963
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : We present a supervised learning-based approach for subpixel motion estimation which is then used to perform video super-resolution. The novelty of this work is the formulation of the problem of subpixel motion estimation in a ranking framework. The ranking formulation is a variant of classification and regression formulation, in which the ordering present in class labels namely, the shift between patches is explicitly taken into account. Finally, we demonstrate the applicability of our approach on superresolving synthetically generated images with global subpixel shifts and enhancing real video frames by accounting for both local integer and subpixel shifts.