Super Resolution Using Graph-Cut

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.

• Type: Conference
• Year: 2006
• Where: ACCV
• Authors: Uma Mudenagudi, Ram Singla, Prem Kumar Kalra, Subhashis Banerjee
• Keywords: ACCV 2006, Computer Vision, Low Resolution Images, Point Spread Function, Reconstruction Based Approach