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.

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subpixel motion estimation video superresolution classification and regression shift patches local integer and subpixel shifts superresolving synthetically generated images ranking formulation supervised learningbased approach

Huang Thomas S,Petrovic Nemanja,Rajaram Shyamsundar,Das Gupta Mithun,.Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution. 2007 (1),.

Huang Thomas S,Petrovic Nemanja,Rajaram Shyamsundar,Das Gupta Mithun,"Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution" 2007

Huang Thomas S,Petrovic Nemanja,Rajaram Shyamsundar,Das Gupta Mithun,"Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution"