An adaptive L1-L2hybrid error model to super-resolution

Huihui Song; Lei Zhang; Peikang Wang; Kaihua Zhang; Xin Li

doi:10.1109/ICIP.2010.5651498

An adaptive L₁-L₂hybrid error model to super-resolution

Huihui Song, Lei Zhang, Peikang Wang, Kaihua Zhang, Xin Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

26 Citations (Scopus)

Abstract

A hybrid error model with L1and L2norm minimization criteria is proposed in this paper for image/video super-resolution. A membership function is defined to adaptively control the tradeoff between the L1and L2norm terms. Therefore, the proposed hybrid model can have the advantages of both L1norm minimization (i.e. edge preservation) and L2norm minimization (i.e. smoothing noise). In addition, an effective convergence criterion is proposed, which is able to terminate the iterative L1and L2norm 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.

Original language	English
Title of host publication	2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages	2821-2824
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2010.5651498
Publication status	Published - 1 Dec 2010
Event	2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: 26 Sept 2010 → 29 Sept 2010

Conference

Conference	2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/Territory	Hong Kong
City	Hong Kong
Period	26/09/10 → 29/09/10

Keywords

Convergence criterion
L norm 1
L norm 2
Super-resolution

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

More information

10.1109/ICIP.2010.5651498

Cite this

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abstract = "A hybrid error model with L1and L2norm minimization criteria is proposed in this paper for image/video super-resolution. A membership function is defined to adaptively control the tradeoff between the L1and L2norm terms. Therefore, the proposed hybrid model can have the advantages of both L1norm minimization (i.e. edge preservation) and L2norm minimization (i.e. smoothing noise). In addition, an effective convergence criterion is proposed, which is able to terminate the iterative L1and L2norm 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.",

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AB - A hybrid error model with L1and L2norm minimization criteria is proposed in this paper for image/video super-resolution. A membership function is defined to adaptively control the tradeoff between the L1and L2norm terms. Therefore, the proposed hybrid model can have the advantages of both L1norm minimization (i.e. edge preservation) and L2norm minimization (i.e. smoothing noise). In addition, an effective convergence criterion is proposed, which is able to terminate the iterative L1and L2norm 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.

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