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 proceedingConference article published in proceeding or bookAcademic researchpeer-review

22 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 languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages2821-2824
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sep 201029 Sep 2010

Conference

Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
CountryHong Kong
CityHong Kong
Period26/09/1029/09/10

Keywords

  • Convergence criterion
  • L norm 1
  • L norm 2
  • Super-resolution

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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