Multimodal image fusion via sparse representation with local patch dictionaries

Minjae Kim; David K. Han; Hanseok Ko

doi:10.1109/ICIP.2013.6738268

Multimodal image fusion via sparse representation with local patch dictionaries

Minjae Kim, David K. Han, Hanseok Ko

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 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 language	English
Title of host publication	2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages	1301-1305
Number of pages	5
DOIs	https://doi.org/10.1109/ICIP.2013.6738268
Publication status	Published - 2013
Event	2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia Duration: 2013 Sept 15 → 2013 Sept 18

Publication series

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

Other

Other	2013 20th IEEE International Conference on Image Processing, ICIP 2013
Country/Territory	Australia
City	Melbourne, VIC
Period	13/9/15 → 13/9/18

Keywords

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

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/ICIP.2013.6738268

Cite this

Multimodal image fusion via sparse representation with local patch dictionaries. / Kim, Minjae; Han, David K.; Ko, Hanseok.
2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings. 2013. p. 1301-1305 6738268 (2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, M, Han, DK & Ko, H 2013, Multimodal image fusion via sparse representation with local patch dictionaries. in 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings., 6738268, 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, pp. 1301-1305, 2013 20th IEEE International Conference on Image Processing, ICIP 2013, Melbourne, VIC, Australia, 13/9/15. https://doi.org/10.1109/ICIP.2013.6738268

@inproceedings{0723cf26a2374929a041ba25a9f6065b,

title = "Multimodal image fusion via sparse representation with local patch dictionaries",

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.",

keywords = "Dictionary learning, Image fusion, K-SVD, Non-local means denoising, Sparse representation",

author = "Minjae Kim and Han, {David K.} and Hanseok Ko",

year = "2013",

doi = "10.1109/ICIP.2013.6738268",

language = "English",

isbn = "9781479923410",

series = "2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings",

pages = "1301--1305",

booktitle = "2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings",

note = "2013 20th IEEE International Conference on Image Processing, ICIP 2013 ; Conference date: 15-09-2013 Through 18-09-2013",

}

TY - GEN

T1 - Multimodal image fusion via sparse representation with local patch dictionaries

AU - Kim, Minjae

AU - Han, David K.

AU - Ko, Hanseok

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Dictionary learning

KW - Image fusion

KW - K-SVD

KW - Non-local means denoising

KW - Sparse representation

UR - http://www.scopus.com/inward/record.url?scp=84897756794&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84897756794&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2013.6738268

DO - 10.1109/ICIP.2013.6738268

M3 - Conference contribution

AN - SCOPUS:84897756794

SN - 9781479923410

T3 - 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

SP - 1301

EP - 1305

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

T2 - 2013 20th IEEE International Conference on Image Processing, ICIP 2013

Y2 - 15 September 2013 through 18 September 2013

ER -

Multimodal image fusion via sparse representation with local patch dictionaries

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this