Ordinal regression based subpixel shift estimation for video super-resolution

Authors:
Mithun Das Gupta;Shyamsundar Rajaram;Thomas S. Huang;Nemanja Petrovic
Affiliations:
Department of Electrical and Computer Engineering, University of Illinois, Urbana Champaign, IL;Department of Electrical and Computer Engineering, University of Illinois, Urbana Champaign, IL;Department of Electrical and Computer Engineering, University of Illinois, Urbana Champaign, IL;Google Inc., New York, NY
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2007

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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.