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.
Super Resolution Using Graph-Cut
Computer Vision – ACCV 2006 ; 4 ; 385-394
Lecture Notes in Computer Science ; 3852 , 4
2006-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Point Spread Function , Super Resolution , Blur Kernel , Image Formation Process , Edge Spread Function Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
Super Resolution Using Graph-Cut
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