Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22706
Title: AN adaptive L1–L2 hybrid error model to super-resolution
Authors: Song, H
Zhang, L 
Wang, P
Zhang, K
Li, X
Keywords: L1 norm
L2 norm
Super-resolution
Convergence criterion
Issue Date: 2010
Publisher: IEEE
Source: 2010 17th IEEE International Conference on Image Processing (ICIP), 26-29 September 2010, Hong Kong, p. 2821-2824 How to cite?
Abstract: A hybrid error model with L1 and L2 norm minimization criteria is proposed in this paper for image/video super-resolution. A membership function is defined to adaptively control the tradeoff between the L1 and L2 norm terms. Therefore, the proposed hybrid model can have the advantages of both L1 norm minimization (i.e. edge preservation) and L2 norm minimization (i.e. smoothing noise). In addition, an effective convergence criterion is proposed, which is able to terminate the iterative L1 and L2 norm minimization process efficiently. Experimental results on images corrupted with various types of noises demonstrate the robustness of the proposed algorithm and its superiority to representative algorithms.
URI: http://hdl.handle.net/10397/22706
ISBN: 978-1-4244-7992-4
978-1-4244-7993-1 (E-ISBN)
ISSN: 1522-4880
DOI: 10.1109/ICIP.2010.5651498
Appears in Collections:Conference Paper

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