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Proceedings Paper

Research on super-resolution based on random fields for low-level vision
Author(s): Min Li; Xiaofeng Liu
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Paper Abstract

The goal of single-frame Super-Resolution is to improve the spatial resolution of a given low-resolution image. However, it is ill-posed. Regularization which can be interpreted as the way of finding the prior distribution of images plays a crucial role in solving this problem. Example-based approach is one of the well-established regularization techniques for image process based on the prior information stored in the database, which is also used for image Super-Resolution reconstruction. This paper previews the Exampled-based Super-Resolution approach which is based on Freeman's work. We show how the example images to be used to generate training set, describing the Super-Resolution synthesis processing based on the training set, with the plausible experiment results on the single-frame image scale-up. Finally, the related problems and future challenges in this field are also mentioned.

Paper Details

Date Published: 30 October 2009
PDF: 5 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74962E (30 October 2009); doi: 10.1117/12.833735
Show Author Affiliations
Min Li, Univ. of Electronic Science and Technology of China (China)
Guilin Academy (China)
Xiaofeng Liu, Univ. of Electronic Science and Technology of China (China)
Sichuan Univ. of Sciene and Engineering (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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