Multi-focus image fusion via coupled dictionary training

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units

  • Northeastern University


A novel multi-focus image fusion approach using coupled dictionary training is proposed. It exploits the facts that (i) the patches in example data can be sparsely represented by a couple of over-complete dictionaries related to the focused and blurred categories of images and (ii) merging such representations is better than just selecting the sparsest one in the estimate of the original image. Inspired by these observations, we enforce the similarity of sparse representations between the focused and blurred image patches by jointly training the coupled dictionary, and then fuse these representations to generate an all-in-focus image by a fusion rule. The key characteristics of our approach are bridging the gap between coupled dictionaries, combining plain averaging and «choose-max» as an appropriate fusion rule, and forming a more accurate representation, compared to existing approaches which simply admit sparse representation over one dictionary. Extensive experimental comparisons with state-of-the-art multi-focus image fusion algorithms validate the effectiveness of the proposed approach.


Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Subtitle of host publicationProceedings
Publication statusPublished - 18 May 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Shanghai, China
Duration: 20 Mar 201625 Mar 2016
Conference number: 41

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X


ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2016
Internet address

    Research areas

  • coupled dictionary training, Image fusion, K-SVD, multi-focus image, sparse representations

ID: 4850977