Super resolution using graph-cut

Authors:
Uma Mudenagudi;Ram Singla;Prem Kalra;Subhashis Banerjee
Affiliations:
Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India;Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India;Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India;Department of Computer Science and Engineering, Indian Institute of Technology Delhi, New Delhi, India
Venue:
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Year:
2006

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Abstract

This paper addresses the problem of super resolution – obtaining a single high-resolution image given a set of low resolution images which are related by small displacements. We employ a reconstruction based approach using MRF-MAP formalism, and use approximate optimization using graph cuts to carry out the reconstruction. We also use the same formalism to investigate high resolution expansions from single images by deconvolution assuming that the point spread function is known. We present a method for the estimation of the point spread function for a given camera. Our results demonstrate that it is possible to obtain super-resolution preserving high frequency details well beyond the predicted limits of magnification.