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Multi-focus image fusion based on non-negative sparse representation and patch-level consistency rectification
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Qiang, Zhang, Li, Guanghe, Cao, Yunfeng and Han, Jungong (2020) Multi-focus image fusion based on non-negative sparse representation and patch-level consistency rectification. Pattern Recognition, 104 . 107325. doi:10.1016/j.patcog.2020.107325 ISSN 0031-3203.
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WRAP-multi-focus-image-fusion-based-non-negative-sparse-Han-2020.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (2462Kb) | Preview |
Official URL: https://doi.org/10.1016/j.patcog.2020.107325
Abstract
Most existing sparse representation-based (SR) fusion methods consider the local information of each image patch independently during fusion. Some spatial artifacts are easily introduced to the fused image. A sliding window technology is often employed by these methods to overcome this issue. However, this comes at the cost of high computational complexity. Alternatively, we come up with a novel multi-focus image fusion method that takes full consideration of the strong correlations among spatially adjacent image patches with NO need for a sliding window. To this end, a non-negative SR model with local consistency constraint (CNNSR) on the representation coefficients is first constructed to encode each image patch. Then a patch-level consistency rectification strategy is presented to merge the input image patches, by which the spatial artifacts in the fused images are greatly reduced. As well, a compact non-negative dictionary is constructed for the CNNSR model. Experimental results demonstrate that the proposed fusion method outperforms some state-of-the art methods. Moreover, the proposed method is computationally efficient, thereby facilitating real-world applications.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
Library of Congress Subject Headings (LCSH): | Multispectral imaging, Image processing -- Digital techniques, Signal processing -- Digital techniques, Pattern recognition systems, Logic design -- Data processing, Sensor networks, Computer vision | |||||||||
Journal or Publication Title: | Pattern Recognition | |||||||||
Publisher: | Pergamon | |||||||||
ISSN: | 0031-3203 | |||||||||
Official Date: | August 2020 | |||||||||
Dates: |
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Volume: | 104 | |||||||||
Article Number: | 107325 | |||||||||
DOI: | 10.1016/j.patcog.2020.107325 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 7 April 2020 | |||||||||
Date of first compliant Open Access: | 9 March 2021 | |||||||||
RIOXX Funder/Project Grant: |
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