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An MRF-based approach to generation of super-resolution images from blurred observations

DSpace at IIT Bombay

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Title An MRF-based approach to generation of super-resolution images from blurred observations
 
Creator RAJAN, D
CHAUDHURI, S
 
Subject superresolution
restoration
expansion
super-resolution
image restoration
markov random field
 
Description This paper presents a new technique for generating a high resolution image from a blurred image sequence; this is also referred to as super-resolution restoration of images. The image sequence consists of decimated, blurred and noisy versions of the high resolution image. The high resolution image is modeled as a Markov random field (MRF) and a maximum a posteriori (MAP) estimation technique is used for super-resolution restoration. Unlike other super-resolution imaging methods, the proposed technique does not require sub-pixel registration of given observations. A simple gradient descent method is used to optimize the functional. The discontinuities in the intensity process can be preserved by introducing suitable line processes. Superiority of this technique to standard methods of image expansion like pixel replication and spline interpolation is illustrated.
 
Publisher KLUWER ACADEMIC PUBL
 
Date 2011-08-17T03:25:16Z
2011-12-26T12:55:18Z
2011-12-27T05:44:05Z
2011-08-17T03:25:16Z
2011-12-26T12:55:18Z
2011-12-27T05:44:05Z
2002
 
Type Article
 
Identifier JOURNAL OF MATHEMATICAL IMAGING AND VISION, 16(1), 5-15
0924-9907
http://dx.doi.org/10.1023/A:1013961817285
http://dspace.library.iitb.ac.in/xmlui/handle/10054/9724
http://hdl.handle.net/10054/9724
 
Language en