Multimodal image fusion via sparse representation with local patch dictionaries

Minjae Kim, David K. Han, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Sparse representation is a promising technique for the field of image processing and pattern recognition. It generally exploits over-complete dictionaries which is fixed and known in advance, or learned using training algorithm such as K-SVD. In this paper, we propose a new multimodal image fusion approach based on the sparsity model with local patch dictionaries generated directly from input images. For every location in the image, dictionary is simply constructed with neighboring patches. Experimental results show that the proposed method is efficient and competitive with some existing image fusion methods.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages1301-1305
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 2013 Sep 152013 Sep 18

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Other

Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period13/9/1513/9/18

Keywords

  • Dictionary learning
  • Image fusion
  • K-SVD
  • Non-local means denoising
  • Sparse representation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Kim, M., Han, D. K., & Ko, H. (2013). Multimodal image fusion via sparse representation with local patch dictionaries. In 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings (pp. 1301-1305). [6738268] (2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings). https://doi.org/10.1109/ICIP.2013.6738268